Dipole Neurology

“The relativistic Brain”: Nicolelis & Cicurel have replicated the finding of NAAMF brain structures and its hybrid physics derivation the” information engine”

2015-06-22T00:37:00.002+01:00

In a highly unexpected breakthrough for this work, world renowned neuroscientist Miguel Nicolelis and Co-author Blue brain project mathematician Ronald Cicurel have published a book which explains how their research replicates the central set of hypothesis in Dipole Neurology theory.

They have validated

The concept of the brains white matter having a magnetic mechanism called Neuronal Electro Magnetic Field (NEMF). NEMF is similar to the developmental mechanism I modified from Neuromagnetic Researcher Dr Marcos Banaclocha called Neuronal Activity Associated Magnetic fields (NAAMF). Dr Banachlocha was one of the pre-reviewers of my 2009 works on this area where I co-authored with Dr Wajid Zia. In 2009 we presented our analysis of the brains neuronal content (in magnetic distribution terms) to the sanger institute conference “integrative approaches to brain complexity” (See Lanzalaco & Zia 2009a, 2009b in references)

That the white matter which we can observe has a magnetic structure and function which defines the principles for neural oscillations as an intrinsic white matter property. (see my table of white matter oscillations used in several of my papers here, (See Lanzalaco 2015; Pissanetzky 2013 in references))

They then concur that we should conceive of the primary (cortico-limbic) structure in terms of a division into grey/white matter to derive a similar type of fundamental physics hybrid dual process “computation engine” (see Section 4 Lanzalaco & Pissanetzky 2013 in references). Like the action entropy “information engine” (which refers to grey/white matter) I worked on falsifying with Professor Pissanetky it computes just by providing it a means to follow the most fundamental laws of physics. Relativism and Action-entropy are different interpretations of the spacetime frameworks..

That the computation engine is of such a form we can use it to guide brain simulation and AGi projects. However we veer paths here as it is still not clear to myself (even as a higher order system) why these propose this problem is uncomputable (as apposed to less tractable). See my latest paper to Future of Humanity Institute (FHI), where I mention the white matter in terms of synchronization and computability issues (See Lanzalaco 2015 in references)

So from similar premises, the same set of logical conclusions result. However there are also differences between our works but overall these are two sides of the same argument. i.e. Differences about: The specific mechanisms then, conclusions on neural coding and computability. There is not space to go into all this here, but this is a table which summarizes all this.

Showdown time ! Comparison chart of these two similar general physics

theories for the brain. But there are differences

Relativistic vs Langranian thermodynamics, different sides of the same coin via the Hamiltonian ?

A point to note here is that relativistic view of spatiotemporal events or an action-entropy classical view are two sides of the same coin, for neuroscience purposes anyway. Relativity is derived from action as are EM and Magnetic fields and we can vice versa derive an action principle for Relativistic MHD (See Physics derivations in references). The reason I opted to put the formalization (Link to our 2013 JAGI paper) in terms of action-entropy was stated in the introduction of that paper. Basically Action-Entropy approach covers all bases. It allows us to make some distance from the original EM framework to determine if other aspects of it can be tested independently by the Causal logic approach used. Second, that action is closer to thermodynamics and we can also derive EM from it, giving more options. And lastly that the least action principle was used by my Co-author Sergio Pissanetzky for his biophysics approach to AGi and general computation which we applied to formalize, unravel and test the dual process hybrid physics information engine.

Spatiotemporal events are still present except action is defined in groupoids as casual sets of spatial vectors and entropy is the arrow of time. We can still define a quantum harmonic oscillator with action in groupoids, but relativity could be more useful to help decode synchronous firing. For example, in the brain time is integrated into oscillation bound information so there is a relativistic aspect there. Information bound to beta oscillations will be multiplexed in a different frame reference rate than information bound to theta oscillations. The brain does have mechanisms to resolve conflicts between and integrate these different frame rates, and there are some very specific models for how Dipole neurology predicts the representation of space and time across complicated brain structures, which are alluded to in the previous papers and this website. The entire approach including details for specific brain modules (as well as an educated guess for the brains neural coding schemes) is planned for the next publication.

Magnetic brain models and structure, where we are now with EM fields ?

I have posted a summary of the latest research on axon solitons, white matter, glia and magnetic or ephatic fields here. Some of these are a big area right now, so a quick summary is required of what is being proposed here in biophysics terms. Initially I followed a magnetic model, but have for some time since 2009 primarily concentrated on a neurodevelopment theory for NAAMF. For that I have allowed myself to be guided by the experts in regards to how far on a limb to go with adult neuron function fields. The idea is to be careful not to go off into quantum mind territory, because the problem is always this. Current MEG readings do provide brain wide magnetic field, but these are derived from the sum of individual isolated neurons. There is no mechanism to amplify the magnetic field fall off in a tranverse plane across axons of any significant distance? Many are being developed , but even taking all the newest ideas into consideration the maximum number of axon solitons in a field would be a dozen or more and not brain wide. However, we know that we can cut of the neurons and the white matter oscillations still persist but this could be a network effect that was not controlled for (references in this post).

Mostly we have learned in neuroscience biophysics is all very finely controlled by complex proteins interactions. Does all this finesse rule out any crude generic magnetic field across white matter ? The complexity of ion channels and mechanisms even in the candidate for a magnetic mechanism, (the axon myelinic synapse) suggests these new concepts still obey regular neuroscience complexity. Douglas Field in his address pitched towards the Obama brain initiative does mention the white matter glia could assist slow wave timing coherence, but not by a field model. However as my last summary reminds me, there is a lot still to be found about the axons, and the reasons we have not is due to the lack of tools for that, and these are being developed now. My position is the fields are primarily large during developmental stages then fade to persist in glia but under restriction of myelin as we think. I keep an open mind on what could be going on in developed brains though as the brain has EM structure all the way through it.

Linear multipole expansion is the proposed model for the limbic system morphology. See links in following text for more explanation. For now whats important to bear in mind is that MHD evolving the facility to layer such expansions allow distinct subcortical structures to evolve for adaptive functions, while still retaining whole information coherence through symmetrical continuous waves moving in and out of phase in a linear manner. i.e. Delta, Alpha, Theta, Mu, Beta interactions are fairly even lateral symmetrical phase locks (unlike sporadic asymmetrical cortical gamma). Its elegance is also its simplicity. MHD dipoles and Linear multipole expansions are easily produced by radial glia.

Where do we go from here ?

In their most recent interview on singularity 1 on 1, the authors describe that the labwork, long sought after by myself and many colleagues involved with NAAMF (Lester ingber, Alfredo Pereria, Paul Nunez, Marcos Banachlocha) could gain the financial backing from Duke University to finally settle the biggest validation required for this nearly 15 year old controversial theory. To track the actual MEG readings from white matter to brain wide neural synchronizations. This requires highly expensive equipment and expertise which Professor Nicolelis appears keen to ensure is provided. The publication of their book has prompted a project for many of the worlds leading magnetic brain researchers to form into a “Neuromagnetic research team” setup to further the major challenges of this frontier research, raise the media profile and push for funding. For that reason it is good time to summarize the proven track record of dipole neurology theory in terms of improving with new data.

A scientist is only as good as their last contribution, but there are plenty of unreleased improvements in the pipeline. I aim to contribute the following to the research team for this project. With this replication of results occurring at such a high level in popular neuroscience, hopefully there will be an increase in publication and some more funding towards a general theory for the brain (which works !)

Complete MHD time and space representation at the detailed level of brain modules
Further work on Axon solitons in terms of MHD flux tubes
Multiscale brain models. i.e. From NAND gates to neural partitions with general filters
From dendrites and nodes to population burst codes to entire structural operation i.e. Place-grid cells
Further neurodevelopment and glial models, in the full detail required for that discipline
The suspected underlying high level AGI, computing class and neural coding scheme

REFERENCES

Physics derivations

Dalrymple, D. 2012. The principle of least action. Available electronically online at

http://www.edge.org/response-detail/11722

Deriving the Nonrelativistic Principle of Least Action from the Schwarzschild Metric and the Principle of Maximal Aging

Edwin F. Taylor

http://www.eftaylor.com/pub/GRtoPLA.pdf

Sylva Poirier

http://settheory.net/least-action

The Least Action Principle and Relativistic Mechanics

http://arxiv.org/ftp/physics/papers/0512/0512110.pdf

An Action Principle for Relativistic MHD

http://arxiv.org/abs/1501.07829

http://www.scholarpedia.org/article/Principle_of_least_action#Relativistic_Systems

See also summary of least action derivations from EM fields in

http://www.degruyter.com/view/j/jagi.2013.4.issue-3/jagi-2013-0006/jagi-2013-0006.xml?format=INT

Lanzalaco, F., Pissanetzky, S. 2013. Causal Mathematical Logic as a guiding framework. Journal of Artificial General Intelligence.

Lanzalaco, F & Zia, W. 2009a. Mechanisms for a cortical dipole structure by VZ calcium waves. arNQ

Lanzalaco, F & Zia, W. 2009b. Dipole Neurology an electromagnetic multipole solution to brain structure, function and abnormality. Wellcome trust, Hinxton, Cambridge, UK.

Cicurel, R & Nicolelis, M.A.L. 2015. The relativistic Brain. Kios Press.

New paper “Neural foundations for the classification of AGi and Superintelligent systems”

2015-06-21T21:45:00.005+01:00

Available online : https://drive.google.com/file/d/0B_wcM5ZfmEE5aWlKN3U1WUtlbk0/view

“Abstract

A proposal is made to justify the utilization of simplified models for self awareness to be used as a classification for Artificial General Intelligence (AGi) and Superintelligent (Si) systems. These models are derived from entire neural topologies and their respective neural markers such as cognitive processes and biophysical signals. Self Awareness is defined generally and then in network terms. Current proofs for AGi-Si development are reviewed and these cast doubt on the predictive power of current algorithmic methods to guide the control and understanding of AGi-Si development. The benefit of computational neuroscience methods are expanded upon further in terms of their detail and depth representing likely actual AGi-Si development. It is concluded that evidence exists to justify exploring the use of general guiding frameworks for AGi-Si classifications which are derived from computational neuroscience”

This paper was inspired by the press hype around dangers from artificial intelligence and summarizes some of the ideas I have on whether brain structures can tell us anything deterministic about the nature of general intelligence. For that we need to look at various proof systems. It is proposing a classification system for all general intelligence system may be deterministic and was submitted as part of a research grant application to FHI Oxford. The central concept disagrees with FHI press position to some degree. Primarily because my work tells us simplifications of brain structures tell us something pivotal about the dual process nature of general intelligence. And not only that, but uf general intelligence has an optimal physical form then AGi has certain types of topology. The work is still in rough shape. I will upload to ARXIV when I Iron out some of the conclusions and re-do the proofs. It has some rough similarities to “TheUniverse of Minds” by ROMAN V.YAMPOLSKY in terms of reference to mind classifications and computational equivalence. I wasn’t aware of his paper till later however, but my proposal is still different in that I insist we impose a physical grounding, especially so for self improving systems that will have more of an issue when dealing with physical limitations.

Digging out more from MHD brain theory for general computation

Although the first general framework was sketched in causallogic terms in the 2013 paper this framework is a general physics grounding, and it was always stated the MHD theory would have to get the game up and explain more complicated implementations at multiple scales in brain structure and function. This is a big project in progress right now, but its over-due to make some general statements on where it is going, without getting into which neural coding schemes are being evaluated.

If we look at the most prominent neural processing features, they scale across three primary neural levels. In order in diagram above (Hausers dendritic computations) 1. The logical NAND functions and filters within neurons. 2. These can then form entire libraries of logical arrays and analogue style resonant filter banks at the population level (some of the classifications izhikevich) 3. Across the entire brain at macroscopic level general filtering is thought to occur in multiplexed action selection, where the hemispheres increase speed of switching sides to deal with more difficult problem tasks.

None of this reveals any general coding scheme in detail, and I propose we will require physics models for that. For now there what we comment on is that the highest level general multiplexing in action selection completely belies the massive number of underlying neuron banks capable of doing something similar. There are analogies to these filter banks and logical physical components used in Deep learning, not surprising considering the neural roots of deep learning. What is going on in the coritco-limbic “information engine” at the overall level is still something many of us are working on. We have some rough ideas though and suspect large scale architecture is the key to our general abilities on deep problems.

There is a body of literature to suggest that human performance on NP problems is good although not optimal approximate results do occur (See Humans on NP in references). Knapsack, Travelling sales problem and graph colouring are something we evolved for to travel, hunt and deal with finite resources. It also appears that in comparison to the same difficult level on P space problems (i.e number of nodes) we may be better at NP problems, which is the inverse for classical von Neumann architecture (with the same working memory to node ratio). So we may be using a generalized NP engine for all problems including P-space. However this is a complicated area and contentious to propose right now, as the landscape of computational complexity had many overlapping facets.

What we do know is the brain architecture is very different due to its parallel topologies. The class of NP problems is also very amenable to parallel matrix computations and quantum computers also leverage parallelism. i.e. Hyperconnected quantum states provide traction on these dense graph type problems, which is not surprising (left and middle in image above). However classical computer architecture is also evolving towards similar hyperconnected states (supercomputer toroidal setups, right in image above) so it could be there is no mystery about quantum computing, as it basically facilitates hyperconnected states and we then leverage this at some given resolution. The gap could be currently closing between the two broad classes of serial and parallel hardware. What does this mean for the brain ? As I have stated repeatedly on my papers and this site for too many reasons to go into, it is not a quantum computer. But the fact is has a magnetic structure (which has quantum structure) arising in neurodevelopment has endowed it with hyper-connected parallelism as part of a hybrid entropy-action system (derived from white/grey matter respectively)

What we can see is that the corpus callosum has the toroidal structure. And the limbic system also has similar network properties (see this summary). If we look at the association areas of the brain they are wide ranging and use the largest white matter loops. This probably facilitates wide across network breadth searches while also maintaining columns with local order. So even with massive internal complexity that grows across species, this basic magnetic structure type physics via billions of connections through axon solitons, allows overall computational coherence and fast, synchronized integration of signals. The coding scheme itself we are still figuring out, there are many candidates to be tested. The good news for this project is that MHD structure does reveal one of the most powerful natural coding schemes known. This will be highlighted in a future publication.

REFERENCES

Discrete optimization using quantum annealing on sparse Ising models

http://journal.frontiersin.org/article/10.3389/fphy.2014.00056/full

AI-Complete, AI-Hard, or AI-Easy: Classification of Problems in Artificial

http://www.academia.edu/1419272/AI-Complete_AI-Hard_or_AI-Easy_Classification_of_Problems_in_Artificial

Human performance on NP problems

Human Performance on Hard Non-Euclidean Graph Problems: Vertex Cover

https://web.uvic.ca/psyc/masson/CMS12.pdf

Measuring Human Performance on Clustering Problems: Some Potential Objective Criteria and Experimental Research Opportunities

http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1001&context=jps

Human Performance on the Knapsack Problem

https://books.google.co.uk/books/about/Human_Performance_on_the_Knapsack_Proble.html?id=SF5TQwAACAAJ&hl=en

Human performance on the traveling salesman problem. Percept Psychophys. 1996 May;58(4):527-39.

http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1090&context=jps

MacGregor JN1, Ormerod T.

MacGregor, James N. and Chu, Yun (2011) "Human Performance on the Traveling Salesman and Related Problems: A Review," The Journal of Problem Solving: Vol. 3: Iss. 2, Article 2.
http://dx.doi.org/10.7771/1932-6246.1090

Neuro images

http://pages.cs.wisc.edu/~tvrdik/6/html/Section6.html#AAAAAEmbeddings%20into%20meshes%20and%20tori

http://brain.oxfordjournals.org/content/brain/128/10/2224/F1.medium.gif

http://brain.oxfordjournals.org/content/brain/128/10/2224.full.pdf

Application of bio-inspired algorithm to the problem of integer factorisation

http://cecs.louisville.edu/ry/Application.pdf

EM solitons proven in the brains axons. One of the primary tenets predicted by Dipole neurology framework.

2015-06-21T19:27:00.000+01:00

If this theory is proposing a magnetic brain structure from the entire morphology that includes Magnetohydrodynamic (MHD) field lines, then there is no getting away from the prediction that the axon bundles will if at least be obeying laws of a larger MHD system. Or maybe even axons or their glial cells are actual MHD generators. Other authors in this area have proposed magnetic fields might occur at a long range (even across hemispheres etc) in adult brains. Marcos Banaclocha's Neuronal Activity Associated Magnetic fields (NAAMF) 2003, then expounded 2007. Following on, were increase in publications on biophysics of glia I refer to: Ingber & Nunez, 2010 , Bokkon & Banaclocha, 2010 ,Pereira 2012, Størmer & Laane, 2009, Størmer et al,, 2011. Prof Ingber of Caltech supports the proposal( recent paper) . Pereira & Furlan provide a more neuroscience based overview for glial magnetic processes. However this is something I have restrained from proposing beyond neurodevelopment except in the limited form. Primarily due to the lack of evidence and various theoretical restrictions from my framework. Primarily that the MHD field drop off is to steep to go anywhere (without non neuron mechanisms) and the fact that dipole neurology is predicting an adult field should be very long range. However a question has always puzzled me. It went like this

Does MHD behaviour play a role in the morphological form and function of axon bundling right across the large scale symmetry of the brains white matter ? Particularly if in the developed brain some complex context that can be understood in terms of these four complex levels most neuroscience theories will have to be explained in.

Neuron-axon electrophysiology. i.e. Ion channels, gap junctions, plasma fluids, membranes, synapses etc
Is the biophysics proposed at Developmental vs Adult level
How does it incorporate Glia of different types and vascular system
Gene transcription, Cellular messaging. Cellular energy and protein transport

The big problem is biophysics has always been found to be deeply intertwined and controlled in these contexts of increasing priority (1-4). And also facets of complex system theory and computational theory are involved here, which are also different in form from morphologies we derive from simple physics. For example if proposing magnetic mechanisms for an entire brain, then the big question is, are axons a direct morphology from function in the sense of representing magnetic field lines in some way ? If they are then there are simply no primary structural morphological features left in the corticolimbic system that are now not well explained in terms of magnetic fields playing an increasingly major role in neurodevelopment. e.g. See this poster. Of course we are still left with so many outstanding theoretical holes, but some new evidence also which will be summarized here.

The first confirmation of MHD structures (as HD in this study only). See this post for summary

What I did find evidence for in terms of long range biophysics to explain white matters magnetic structure appearance was entire hemisphere Ca2+ pulsing through the radial glial scaffold. (see in references Weissman, TA et al; 2004) .So the dipole neurology theory since that time has been driven by study of glial biophysics, rather than the neuron/axon. The big news here is that axons have recently been proven to be MHD in form and function. Due to the development of an extension to the Hodgkin Huxley model for nerve transmission called the soliton model . This resolves some long standing problems over axon thermodynamics. There has been some controversy over the Axon soliton model since proposed by Thomas Heimburg and Andrew D. Jackson in 2005, but last year the Membrane Biophysics Group at neils bohr institute confirmed that solitons do pass through each other as predicted by the theory. Here we should emphasize that the axon soliton is equivalent to an MHD flux tube soliton. Primarily because by their very nature EM fluids that are symmetrically confined are plasma MHD solitons due to the perpendicular magnetic field. The MHD soliton can be an adiabatic sound wave (see table 1 here) just like the axon soliton and so the axon is now consistent with every other observable morphological neural aspect being magnetic (except for neurons themselves). How does this scale up to magnetic properties across the entire brain structure ? The MHD tubes are the field lines.The first question is to what degree is there, if any coherent magnetic fields across MHD solitons ? Because bundles of tubes wit a single MHD soliton (with some dipole moment) does not always translate to that field strength facilitating an entire magnetic field coming from a white matter structure.

The first confirmation of axon solitons passing through each other - top left, (see “axon solitons” in References). MHD plasma tubes are also solitons in the same way. Amongst alternative neurophysics theorists we are reluctant to entertain others who go to far into quantum mind, however the fact is Matti Pitkanen of University of Helsinki, has got it right here (top right in image above) at least in this particular case. The images below are mathematical calculations to determine if solitons can cross axons in a transverse manner, as there is now intense interest in axon models which use solitons or other means to generate tranverse waves across the axon bundles (see “axon solitons” in References)

Can there be magnetic fields across white matter bundles or the entire brain ? We do get MEG readings for such wide areas and they can be synchronized oscillations, (see “Magnetic fields at population level” in References) but our evidence so far is that each neuronal component contributes isolated activity. The framework for oscillations to occur across the brain is laid down in neurodevelopment and its this framework that is triggered by neuronal ensembles. The mechanism for Dipole neurology theory providing a magnetic field to create entire field structure is in the Ca2+ waves of the radial glia scaffold in development, there is no myelin when the brain is developing, so fields provided by the Radial Glia would be less restricted. The axons are made primarily of Ferroelectric microtubules and ion channels so would conform easily to any complex MHD field. After the pathfinding has taken place the Oligodendrocytes (white matter glial cells) start to coat the cortico-limbic nerves up to 20 at a time. So in these circumstances there should be a reduced magnetic field.

Magnetic field lines turn out to be flux tubes and can be solitons if the form is plasma, but do these help us to understand axon formation ?. Axons are marked by their regular linear bundling (note these bundles are not from the corpus callosum shown above)

In the developed brain radial glial scaffold fades to the existing astrocytes and oligliodendrocytes, and we are left speculating over whether there is any magnetic field in developed brains, primarily because neurons, axons themselves don’t provide such long range fields. Much of this speculation and various models are documented on this site and the 2009 paper. Most of the recent developments on ephatic fields etc again appear to rely on the glial mechanisms, but without more work we cannot be sure. As axonal ephatic models show (see “axon ephatics” and “axon solitons” in References) the more synchronized axons are added in a coupled state the greater the decrease in conduction speed, and this may be a principle of mutual information in physics. Because relatively speaking within the entire brain system itself lower speed = greater entropy due to the competitive dynamics increasing speeds locally. This may be the reason that the brain wide oscillations tend to be slower, and the local fast oscillation tend to decoherence. This is most notable at the brains primary poles where the fast ripples are the sharpest waves. This is one reason I define oscillations in terms of entropy (see JAGI article 2013).

Artists impression of dipole neurology framework simplified in terms of oscillations. Note how the most integrative long ranging entropic oscillations tend to slow down settle around the midpoint of a system, and the fast oscillations produce the sharpest waves at the hippocampus

We know a lot about the brains neural oscillations and the major role they play in integrating information. There is still a lot that is not known about how they arise, and why there are more of them where there is more white than grey matter. See my table of oscillations located to matter types here in this blog article.

White matter computation

First there was dendritic computation, then synaptic, now axonal.. what next glial ? It appears so. Now is a good time to summarize the fact that white matter in particular its glial cells is a current frontier field of neuroscience. Led Primarily by Douglas Fields, author of The other brain (see “douglas fields” in References). Douglas titles one of his papers “white matter, matters” ! He summarizes how it possible to modulate impulse speed and so in theory effect neural synchronization in white matter, by injecting current into oligodendrocytes. This is called the axon myelinic synapse (see “axon computation” in References).

Douglas fields at Glial Biology in Learning and Cognition, held at the US National Science Foundation in Arlington, Virginia

“The complex branching structure of glial cells and their relatively slow chemical (as opposed to electrical) signalling in fact make them better suited than neurons to certain cognitive processes. These include processes requiring the integration of information from spatially distinct parts of the brain, such as learning or the experiencing of emotions, which take place over hours, days and weeks, not in milliseconds or seconds”

There is now evidence for non synaptic computational properties between axons which do the following (see Debanne and Sami Boudkkazi under “axon computation” in References)

1. Integration and amplification 2. Routing 3. Inhibition 4. Network resonance 5. Synchronization 6. Plasticity

Left: Diagram from Douglas fields showing how action potentials can be inititated across the glial cells. Right: This has been named the "axo-myelinic synapse", (see “axon computation” in References).

The outstanding question is still what are the biophysics here ? We know that we can cut of the neurons and the oscillations still persist (see “axon computation” in References). We have touched on ephatic coupling in the myelin being MHD solitons, but what would amplify the magnetic field fall off or enable resonance across axons ? There is clearly still complexity controlled by proteins with a finesse that appears to rule out a typically crude generic magnetic field across white matter, or even fine grained quantum field containing information (in a generic linear sense) because the axons etc are not organized at that level of precision. The complexity of ion channels and mechanisms in the axon myelinic synapse suggests this is still regular neuroscience complexity and the magnetic field which assists magnetic structure formations are developmental. Remember development does not finish until reach age 25. Even for neurodevelopment alone why would this field be present at all ? It is because there are so many desirable properties of MHD solitons to be recruited in the complexities of brain development and computation. Note these MHD properties are also properties present in the white matter.

Oscillation
Symmetry
Domain walls across entire structure
Rope or “tube” flux structure
Magnetic Connection across tubes

What computing functions do all this give rise to ? Hyper-parallelism with deep information convolution across layered structures is my primary idea. The hyperconnected graphs that are good for solving generalized problems in approximate ways such as discrete optimization problems appears to be the reason. But that is another subject, and more of that in the next post.

REFERENCES

Ca2+ waves in development can propogate through entire hemisphere radial glial

Weissman, T.A., Riquelme, P.A., Ivic, L., Flint, A.C., Kriegstein, A.R., 2004. Calcium waves propagate through radial glial cells and modulate proliferation in the developing neocortex. Neuron. 43, 647-61

Axon Solitons

http://phys.org/news/2014-09-nerve-impulses-collide-unaffected.html

http://medicalxpress.com/news/2014-09-spikes-collide-foundation-neuroscience.html#nRlv

http://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.031047

Penetration of Action Potentials During Collision in the Median and Lateral Giant Axons of Invertebrates

https://www.researchgate.net/publication/263789234_NON-LINEAR_SOLITARY_SOUND_WAVES_IN_LIPID_MEMBRANES_AND_THEIR_POSSIBLE_ROLE_IN_BIOLOGICAL_SIGNALING

NON-LINEAR SOLITARY SOUND WAVES IN LIPID MEMBRANES AND THEIR POSSIBLE ROLE IN BIOLOGICAL SIGNALING

http://www.igi-global.com/article/propagation-front-waves-myelinated-nerve/52977

http://arxiv.org/abs/nlin/0610071

Propagation of Front Waves in Myelinated Nerve Fibres: New Electrical Transmission Lines Constituted of Linear and Nonlinear Portions

http://arxiv.org/abs/nlin/0610071

Pulse Dynamics in Coupled Excitable FIbers: Soliton-like Collision, Recombination, and Overtaking

Axon ephatics

http://www.ncbi.nlm.nih.gov/pubmed/19887308

Thresholds for Transverse Stimulation: Fiber Bundles in a Uniform Field

http://link.springer.com/article/10.1007/s00422-003-0430-x

Conduction in bundles of demyelinated nerve fibers: computer simulation

http://link.springer.com/chapter/10.1007%2F978-0-387-22463-3_8

Ephaptic Interactions Among Axons

http://www.macs.hw.ac.uk/~chris/HWM99-40.pdf

Ephaptic Coupling of Myelinated Nerve Fibers

Douglas Fields

White matter matters.

http://www.cs.unc.edu/~styner/public/DTI_tutorial/1%20Scientific%20American%202008%20Fields.pdf

http://www.nature.com/news/neuroscience-map-the-other-brain-1.13654

Neuroscience: Map the other brain

https://science.nichd.nih.gov/confluence/display/snsdp/Regulation+of+myelination+by+neural+impulse+activity

Regulation of myelination by neural impulse activity

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2756778/

Oligodendrocytes Changing the Rules: Action Potentials in Glia and Oligodendrocytes Controlling Action Potentials

Axonal computation

http://www.pnas.org/content/103/42/15646.long

A beta2-frequency (20–30 Hz) oscillation in nonsynaptic networks of somatosensory cortex

“Surgical separation of deep from superficial layers at the layer IV/V border abolished neither rhythm”

https://www.researchgate.net/profile/Sven_Hendrix/publication/226551250_Regeneration_After_CNS_Lesion_Help_from_the_Immune_System/links/0046352b9a3a6dba40000000.pdf

New Aspects of Axonal Structure and Function

See Chapter 4 New Insights in Information Processing in the Axon Dominique Debanne and Sami Boudkkazi

http://www.nature.com/nrn/journal/v5/n4/abs/nrn1397.html

Information processing in the axon

http://www.ncbi.nlm.nih.gov/pubmed/18634564

Modulatory effects of oligodendrocytes on the conduction velocity of action potentials along axons in the alveus of the rat hippocampal CA1 region.

http://www.sciencedirect.com/science/article/pii/S0166223611000920

The axo-myelinic synapse

Magnetic fields at population level

http://link.springer.com/article/10.1007%2FBF01191640#page-1

Task-specific magnetic fields from the left human frontal cortex

http://link.springer.com/article/10.1007%2FBF01190388#page-1

A four sphere model for calculating the magnetic field associated with spreading cortical depression

http://link.springer.com/article/10.1007%2FBF00229864#page-1

Source analysis of magnetic field responses from the human auditory cortex elicited by short speech sounds

http://www.ncbi.nlm.nih.gov/pubmed/18652805

MEG correlates of bimodal encoding of faces and persons' names.

http://www.nature.com/srep/2012/120509/srep00401/fig_tab/srep00401_F2.html

New MEG sensors can detect axons

http://www.nottingham.ac.uk/magres/research/meg-research.aspx

Large scale distributed brain networks identified using MEG measured beta band oscillations.

Application of Casual Mathematic Logic (CML) to brain simulation

2013-05-28T19:16:00.001+01:00

Two of our papers were accepted in the same issue of the Journal of Artificial General Intelligence special edition on WBE and Connectomics. These are available as open access. There are about nine papers in this edition on Whole Brain emulation all tackling different aspects of the mind upload problem. Two been written and co-authored between myself and Prof Sergio Pissanetzky (who came up with CML). Each paper is focused for different aspects of the problem, The first with primary author Prof Pissanetzky is the introduction for CML. The second where I am primary author represents the full realization of the approach described on this site, and is the progress from previous papers regarding how to approach deriving general mammalian computation from the biophysics of brain structure.

But this had halted with a major problem. How consistent was this approach with both fundamental physics and information theory. i.e. If principles of intelligence are intrinsic to the evolution of brain structures that fuse complex amalgamations of proteins and biophysics, then these structural principles should be scaling in some kind of regular manner from fundamental principles of information. Regular neuroscience had no bridging theory for this problem but Causal Mathematical Logic has. CML describes how information algorithms self organize from the most fundamental physical principles (such as least action and entropy). Images below are from the primary paper "Causal Mathematical Logic as a guiding framework for the prediction of "Intelligence Signals" in brain simulations. Authors: Lanzalaco, Felix; Pissanetzky, Sergio"

Here we show how CML theory orders information in a manner analogous to cortical columns, simply by coding the principle of least action.

The application of CML to the "go for everything" approach in the cortico-limbic system summarized on this site, now produces the first step towards a complete "information engine" model that unifies computational neuroscience and Artificial General Intelligence. Its also the first well defined approach to dual process theories in physical/neuro and AGi terms. Some of the justification from the paper follows.

We summarize the brains oscillations, where they are located in the brains topology and whether these phase lock. The finding is most phase locking is limbic system and the clock sources are coming from the brains central structures. Much like an oscillation tends to settle into the equilibrium at the center of a thermodynamic system.

Using CML theory we find the relationship between action (high to low) and entropy. This should predict the primary contents for brain signals, that we had previously formalized action as ERD and entropy ERS. You will need to check the paper for an explanation.

There were some problems (check the paper). But the relationship is verified as similar. Bear in mind this is still a correlation that rests on the papers more refined presumptions about physics of brain structure. Read the paper to find how the physics basis is already existent in many other works.

An attempt at a formalization for this approach in thermodynamics the recruits some definitions from integrated information theory (IIT). This formalization is veerinng away from the direction proposed by Prof Pissanetzky and is influenced by some aspects of Alex Wissner Gross Freer. The idea is that we make action and entropy equivalent. Something which is strongly resisted in the computational sciences right now, primarily because describing and formalizing the processes of order in information is so important. But I dont buy it. Coming from a neuro view I think Alex Gross description of entropy hits on the physics formalization for one part of common dual process formalization for intelligence. For a video of Alex see here, and for a wiki summary of dual process theory see here.

And where next ? This approach has to explain all these brain features in terms of physics and information theory. Many of the above are being considered essential for Artificial General Intelligence. I have been collaborating with other colleagues in regard to this. Again watch this space.

The point regarding brain simulation, is the basic idea is that a brain simulation (or emulation) has to run on a thermodynamically responsive system. Especially as brain simulators become more generic in form. All the process of transcribing the details of the neural parts can be done by current methods, but if its going to be conscious (a word i don't like using).. or better term to "process integrate/ dynamically" and reflect as we do.. it will have to produce EEG to generate Event Related Potentials.. and these are best described or understood by CML (or something similar) in terms of thermodynamics. The good news is this predicts no esoteric quantum physics will be required.. and a thermodynamic brain simulator (or emulator) will hopefully work fine without such extreme details.

The Quantum mind objectors will say that Quantum mechanics is still thermodynamic. My reply is we only need to go for the physical theory which describes the thermodynamic resolution the information operates on. Almost all of the neural mutational complexity produces mechanisms at the synapse to glial level. If the thermodynamic description is enough for the entire physical description at this scale, then that means this proposal is the start of a new general physical model for simulators/emulators based on mammalian structures.

The other paper where Prof Pissanetkzy is the lead other here describes more of the mathematical and philosophical foundations. There is also a supplementary section for the above paper linked to here called "Can CML predict solutions for outstanding questions in Whole Brain Emulation (WBE) ?" This will be updated also with the accepted peer review points.

OTHER DEVELOPMENTS

I was not successful in procuring the labtime from Edinburgh University, to falsify which mechanism produces the dipole flow in development. We need this mechanisms falsified. If is verified we then have a strong justification to propose that the FET flagship Human Brain Project would need to model the cortical column model in the context of the dipole model. This work is competing with many traditional applied projects that are directed towards curing medical disorders. Good news is it appears other researchers in Scotland have been discovering dipole flow in neurodevelopment. i.e. This study from Prof. Timothy Newman at Dundee, College of Life sciences.

A 'chemotactic dipole' mechanism for large-scale vortex motion during primitive streak formation in the chick embryo.Phys Biol. 2011 Aug;8(4):045008. doi: 10.1088/1478-3975/8/4/045008. Epub 2011 Jul 12

So it appears the dipole-multipole concept lab results are appearing as I predicted way back which is vindicating. But as with the work from Vincent Fleury's Lab in CNRS Paris. I am still not in agreement with these labs on the mechanism. That was the point for my lab request. I predict that Ca2+ flow through the Connexin, Pannexin network in and out of the Radial Glia provides Magnetohydrodynamic flow giving rise to generalized cortex wide guidance pulses for electrotonic components. i.e. Guidance molecules, Intracellular ion gradients and astrotactin adhesion for neurons moving along the Glial fibers. There is more on that on this site here. For those new to this concept, I justify the application for this dipole force in development applying to mammals based on the indirect data meta-analysed from my 2009 publication, and the evolutionary roots of the cortex, which pre-date Clade Avialae (birds !) back to the roots in phylum chordata (sea creatures). A post on that issue here, based on the bio-informatic regression of synapse carried out by the genes to cognition project of Seth Grant. There have been other studies summarized by a scientific american article which reached a similar conclusion.

Critique of Tony Wrights Left in the Dark theory

2013-01-27T16:28:00.007+00:00

Not put anything up for a while. Open University is keeping me occupied with math and programming, which will hopefully develop the computational models for my brain structure theories. In meanwhile I found this lost critique I had made of Tony Wrights theory on the development of human hemispheres (my specialty !). Tony has a new book which again does not address my objections to the previous one, so here they are again.

For an overview of Tony Wrights projects.

http://en.wikipedia.org/wiki/Tony_Wright_(sleep_deprivation)#Left_In_The_Dark

I specialize in theory about how brain structure both develops biohysically and the resulting computational principles from this structure. My critique of his work has been re-covered from the following news article citing his video where I made these points to him in a discussion we had but these were deleted later, not by Tony it looks like, but the magazine reformatted all its articles and discussions were lost.

How the Left Hemisphere Colonized Reality Douglas Rushkoff at 7:10 am Sat, Oct 9

http://boingboing.net/2010/10/09/how-the-left-hemisph.html

Word version of critique here

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The "percepto-bit", can we run our experience in silicon at mesoscopic scales ?

2012-09-26T02:28:00.001+01:00

The 2 posts on brain simulation are now rolled into one. Entire Blog on one page at www.Lanzalaco.org.

My whacky idea of the week is a concept called the "percepto-bit" for brain simulation / emulation. That we can base our box models for consciousness at the mesocopic scale where there is the emergent level of the entire variety of brain functions. Neuromdulation nuclei which can alter emotions to a noticable degree start at mesoscale. Recently I was given a brain tissue lesion counting study for a university project. It turns out it is also mesoscale which is the point we actually start to perceive any problem due to the disappearance of brain areas i.e. lesions, plaques, tangles or vascular blocks when they interrupt memory, emotion and phenomenal consciousness are tiny holes in tissue at mesoscale, but we are not impaired (as far as i know) by these problems when they are microscale level tears in brain tissue. What is modeled below "perceptobit" scale will have to contain the full richness of data flow in a real mind, but since this is below perceptual access substrate, the processing and algorithms can be whatever internal approximations we choose. Only above percepto-bit scale has to be brain accurate. As long as the percepto-bits communicate the known internal processes within these bits to each other, why should we perceive any difference to our own substrate ?

I am not currently sure how original this idea is but it would seem controversial to the low micron level proposed as necessary for simulations/emulation. Last month I decided to just rattle of a document on what i thought were the main grounds to cover in brain simulation/emulation. Dr Randal Koene an expert in brain emulation then uploaded a video of private discussion to experts in this field, and all the same points had been covered.

Is mesoscale sufficient to build the substrate of the mind from ? If anybody is familiar with my interest in astroglia field models for columns and Ca2+ waves in neurogenesis, they may be aware, that their properties come together at the mesoscale. Some biophysicist argue that this existance of quantum fields means simulations should be more extreme. i.e. model the brain at nanoscale. If the functional properties are coherent at mesoscale, thats not the case. They actually help bolster the concept to model at larger than micron scale. Perceptobit level is the mesoscale for which any modelling above that is not brain accurate interferes with perception. This is the ground resolution from which information is passed and has to accurately represent brain like signals and physics between each perceptobit. Simulation wise its still pretty intensive to be so physically accurate even at mesoscale, but far easier than microscale.

This had me consider the application of an area I like to specialize in. That the top down biophysics of neural structure might add another simplifying perspective. I don't propose here not to model low micron scale, just that if we cannot perceive a knockout to anything at that level, then whats in that scale only has to input and output the correct kind of perceptual information to other "perceptobits". So we can just use traditional or new sustrate independent modelling methods within that bit. It is a very strange idea that we could exist and enjoy experience unscathed running in silicon modeled to brain accuracy at sub millimeter fine tip of a pinhead scale. A Very strange idea, even to me, but then so were the previous concepts i came up with and look what happened there ! This reasoning is further explained here at Randal Koenes carboncopies group.

Can mesoscale simplify proposed nano-scale field effects for information processing ?

Common objections raised to brain sims regard ephatic EM and magnetic fields holding nano-scale information. This is something I focus on pretty heavily if anybody is familiar with my biophysical proposals for brains structure. If we look at the papers for magnetic mechanisms (folder here), the proposed fields produced by Ca2+ in astrocytes emerge at the mesoscale and are proposed to structure the column itself, while also acting as switching capacitors to sustain sensory signals without them having to constantly re-spike. If we look at some more recent papers for ephatic fields in hippocampus (folder here), and try to build a scenario they appear to be converging towards organizing at theta oscillation in stem cells which eavesdrop for non synaptic go signals. i.e. Again, these do not appear to be fine grained information fields. Their function like the Ca2+ in the columns may just be to provide a generic mesoscopic coherence. What would be its function ? A linear sort across the entire septal temporal hippocampus axis in sleep is proposed by neuro-computationalists. So the idea is a field activation of Ca2+ flow can restart all the stem cells on that axis at the same time. Such coherence has a macroscopic function. To re-enstate a line of fresh neurons, facilitates a linear all at once network re-sort along this entire association zone of the brain. If coherence dropped there would be fragmentation of the autobiographical sequences (composed of episodic codings) which forms our sense of self.

The idea is in sub perceptobit level we cannot perceive if the processing is brain like or not. We can implement each bit distributed on hardware in a grid. Even at Mesoscale each bit will require a lot of processing, perhaps an entire cluster by today's standards. There are so many signal types to be accounted for, Glia, modulators, Neurons, enzyme effects, field effects etc. However one sub perceptual architecture means the requirement of each perceptobit is to present brain like information to its facing neighbor. i.e. Achieve perception by mapping the brain like signals dynamically to each other perceptobit. It can be achived by non brain like computation that fits with the current evolution of computer architecture.

This is speculation of course. I did derive these concepts for field function however by tracking (hypothetically) what I propose are the role of Ca2+ waves in the developmental origins from the radial glia forward in time to when they fade to become the cortical astroctyes and adult ventricular stem cells. Both my proposals for development and those of the independent researchers adult models citied above are "fairly" consistent with each other. There is however a recent sub micron electrostatic field model for CaMKII coding the neurons internal cytoskeleton structure by craddock 2012. If its correct, and the above fields are integrating with this, that maybe problematic. However the end result of that is just the macroscopic neurons structure, so that should be dealt with by sub "pecepto-bit" substrate independent modeling. A more pressing mesoscopic issue to be considered is that of Glia holding the key to large scale structural plasticity.

More reason to up the sim resolution. Glia reframe the computational structure of the neuron model.

What does this concept offer if the idea has some truth in it ? What it cannot do is solve the hardest mind upload problem of data acquisition. A top down view, may help in the second major problem of mind upload/simulation. That is the problem of hardware requirements for real time computation in brain emulation/sim can be brought forward by about ten years if mesoscale is enough to run consciousness. Freeing up computational resources can then deal with many simulation /emulation problems like glia and all the unknowns in neuroscience that are vastly underestimated in this field. i.e. Modeling neurons as the calculation factor for a roadmap is not realistic. The current computational evidence shows us neuron graph models structurally are flatpack without glia.

See the neuron graph models with no structure guidance (A) then with structure guidance (B,C,D). Glia are responsible for self organizing a guidance for the weird morphological structure in the brain, not neurons. You can get basic signals out spiking neurons networks, but no actual computational brain structures.

With glia, neurons are constrained by overarching top down principles imposed on them to develop interesting structure. i.e. They can start to build the dynamic, macroscopic complex computational structures we are familiar with. The latest big research from brain transcriptome centers are unable to determine known cortex structures from the genetic mapping of neurons. What does this mean ? The neuron as the basic component for simulation/emulation is to a degree a faulty concept, but the degree to which it is faulty has amazingly not been realized and our largest simulation/emulation projects are "trying" to run on it ! We do get oscillations, and crude spike co-coalitions, but anything else remotely brain like has to be arranged for the network by programming structure. It does not derive a large brain like network structure from internal basic principles. Why ? A great problem is the various glia have their own unrecognized structure building principles (the focus of my PhD research) which is forcing the greedy growth principles of neurons into large complex structures that are macroscopic scale. So there is then no single entry point or morphology to pick for brain simulation. Ideally then model all the morphology at all levels but being practical thats too power hungry. So a proposal is to realize the problems with the neuron substrate model and compromise to a box substrate model comprised of the baseline size for perceptual bits which communicate the known internal processes (including glia, Ca2+ waves ,+ room for unknowns etc) within these bits to each other.

This mesoscopic scale may not be sufficient, or there may have to be variable scale focus for different brain regions, but if the top down concepts can simplify this problem and bring simulation/emulation timelines forward, why not put the idea out there for consideration. I don't even know if this is an original concept. The idea does seem pretty obvious.
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Some introductory articles on glia. For more on ramifications of glia see this site. Much of the theory on this site is also about the structure that glia builds.

Without Glial Cells, Animals Lose Their Senses

This article shows how neurons collapse without glia, pretty consistent with the computational graph models

Glia Guide Brain Development In Worms

Even in worms the Glia are guiding structure. It is the radial and various glia which gives larger brains their structure.

Brain's Connective Cells Are Much More Than Glue: Glia Cells Also Regulate Learning and Memory

No surprise there. The Ca2+ astrocyte wave models are proposed as a key to short term memory, as well as hippocampal neurogenesis.
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New paper Modality Independent Neuro Development Substrate for Artificial General Intelligence “MINDS for AGi”

2012-08-16T01:34:00.000+01:00

Here is my application of the dipole / multipole framework to AGi for AGI-12, 5th Conference on AGI @ Oxford, Dec 8-11 2012 -- POSTER LINK

Its called The Integration of a Deep Structure Neuromorphic Framework for AGi: Modality Independent Neuro Developmental Substrate for Artificial General Intelligence “MINDS for AGi” - LINK

2b edited 30 pages!

Whats it about ? If you have the entire structure principles, you have generalized map for the entire computational principles. When filled out with low level detail its perfect for the highest level modality free neuro models for AGi. Needs refinement to specific in depth computation, but a start towards high level principles.

Also brief 2 page outline for application of Dipole expansion framework to brain emulation / copies - LINK

Felix Lanzalaco (in picture) presents his long awaited abstract to a paid audience outside in the cold scotland climate. Some youtube levity was needed after writing such a heavy going paper, but is she on to something ? Even more to the point, am I on to something, OR did I slip into groupthink and/or pseudoscience (yes the video was improvised).

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UPDATE 16/09/2012 : As predicted by all the paper was way too long for AGI-12 as its a Springer proceedings Journal with fixed length.. The content itself passed review on all 4 measures used (That surprised me !), but both reviewers rejected on the editorial issue of length. It has not been possible for me to reduce it to 10 pages right now for these reasons (aside from my final neuroscience exams for this year also).

1. Since producing it I have realized the concept still needs fleshed out further, i.e. Its still an over-simplification too far.

2. To shorten to a core would need reference to its original larger work, and this has not been submitted to a repository.

To my surprise one of the reviewers proposes high level architecture is harder to get right than sub system components. Thats what I advocate also, but other AGi programmers think differently on their quest for simple principles. They haven't experienced enough neuroscience in my view. This review point is the primary reason I could not compress the conceptual framework right now. The unique insight I propose to posses into the highest level structure is not enough. There has to be more work to produce consistency with both known principles and known unknowns in neuroscience at all key levels of scale before a compression to high level principles. I already had the plan, and carried out groundwork in how to go about this, thats what the PhD is about. Its no easy task and requires a thesis just to cover the projects scope.

Sorry to say but my current opinion is if the brain is anything to go by even with a full understanding at all levels the final principles for general intelligence are going to have to include the integration of a lot of sub principles, and that would be after such principles have been well untangled from the biological substrate. Maybe there is an E=mc2 for the brain, but I have doubts it can capture the true systems functionality. My hope is the neuro-computation aspect of the biophysical understanding I bring will be similar to the resultant success I experienced with the developmental predictions made almost 10 years ago (from the proposed top down models here). That is that having the final high level insight integrating with the lower-level work of todays neuroscience can make predictions which can inform us in our AGi (brain derived) system research regarding both known/unknowns and the hardest of all, unknown/unknowns.

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Visual morphology used as method by Izhikevich's cutting edge "Brain Corporation" labs

2012-05-31T21:03:00.001+01:00

Dr Eugene M. Izhikevich, the creator of scholarpedia, runs one the worlds leading projects for transcription and simulation of the Mammalian brain in a computer system. He recently employed Dr Hermman cuntz who provided a "greedy growth" algorithmn which describes the structure of neurons based on how they grow with any given space (paper here).

He clearly states "Morphology is key to understanding both circuits and computation, since it reflects the constraints given by both"

or from the video at end of this post

“to have a good model, you need to have information about the shape of the cell (neuron)”.

Below is the result of using this structuralist principle by placing function derived from structure algorithms in cell spaces such as the hippocampus, cerebellum etc.

Paradoxically Nobel Laurate Gerald Edleman who assisted Dr Izhikevich in 2009 on these projects, like many others before and after him, have told me to ditch the approach of using morphology as a guide to function, when i presented the dipole cortex concept to the journal he was working on at that time. Perhaps all members of the team do not see eye to eye on all matters "brain". Regardless of disagreements between new and old schools of thought, the fact is that the work of Izhikevich, Cuntz and Brain corporation relies heavily on structuralism. By doing so they have with a fraction of the budget, produced results that exceed the massively funded human brain project which follows a completely gene-centric position.

The following video about brain corporations work is well worth watching. I have been criticized repeatedly for asking questions of does the cortex dipole like morphology dictate its computational function. Here we see this approach being advocated just now for neurons. I hope to try and persuade these groups at some point we can use Magnetohydrodynamic (MHD) principles within similar constraints to entire structures such as the cortex, cerebellum, limbic regions etc in neurodevelopment, and that the MHD roots of neurodevelopment also has the potential to reveal the still unknown principles behind the parallel wiring of axon bundles.

i.e. to understand connectivity, Izhikevich teams up with Frank C. Hoppensteadt to explore the abstract concept of poly-synchronous wavefronts (here). This is compatible with my developmental view that brain morphology that has highly dense axon connection's with continuous synchrony (such as the limbic areas) are dominated by harmonic interactions that have their roots as Magnetohydrodynamic (MHD) harmonic modes which arise in the ventricular zone. This image gives a visually simple perspective of the concept of such modes.

For more in depth information, I have updated my 2009 post where i show differences between the MHD work I have advocated, and independent findings (based on traditional turing model for development) which have evolved laterly to verify at least part of this concept.

NOTE: I have also updated the findings after i presented my work at the conference "integrative approaches to brain complexity". i.e. Its become clear to me now why these brain simulations projects will fail without top down models.

First lab work shows the Dipole forces shaping the embryonic brain

2011-11-03T09:20:00.022+00:00

>>>> AUTHORS NOTE: This page shows evidence for the neurodevelopmental dipole, for my proposed mechanism see HERE.<<<<<

Vincent Fleury a French embryologist, the Research director at Centre National de Recherche Scientififique in Paris has developed a novel filming technique which can capture the live movements of early gestation chick embryos. His findings propose that the filmed motion is the pulsing from a transition boundary between the dipole/quadrupole field throughout the formation of the head in the chick embryo. This is of course consistent with the predictions the approach advocated here makes since 2004.

Also see this post for proposed EM mechanisms in development

The developmental labs at Dundee university, Scotland are also modelling the dipole flow. Please note theirs is more general to the entire embryo. The point is their modelling also requires on electrostatic or EM dipole to produce an explanation for early developmental forces.

BACKGROUND TO DR VINCENT FLEURY AND STRUCTURALISM

Fleury has long advocated (and written volumes) for a view of neurodevelopment called “structuralism” which was first proposed by Darwin but has been controversial with geneticists (who often misrepresent it by referring to old models) seeking a bottom up solution for evolution. The concept of structuralism as it stands today is that the facility to promote structural integrity is highly conserved to work hand in hand with gene expression for developmental mechanics. i.e. Genes transcribe larger emergent principles or physical structures in what is currently considered a multi-level top down/bottom up system. Fleury and others, argues this conserving for emergent structures is the reason most creatures are tetrapods (four-limbed forms). Fleury studied chemical vortices in early stage Gastrulation to arrive at this position.

The developmental biology Professor PZ myers slated Fleury initially in 2009 as “crackpot” science incapable of producing lab work to back up his claims. Yet Fleury produces competent, in depth works on developmental genetics and his colleages are innovating new lab methods (Myers even misrepresnts, Fleury as being incapable of labwork when Fleurys personal webpage shows many dozens of photos taken in his lab !). Myers then follows this by posting Dr Fleury's artistic attempt to try and express a concept describing the chemical flow. PZ myers clearly is playing foul, making false accusations, then not illustrating this image is an analogy picture. Considering the depth and competence of this work it was contained in, which would be considered a very good introduction to the state of the art in this field, Myers is either incompetent or intent on misrepresentation of these ideas.

OK, back on track. What is interesting is Fleury has independently predicted the brains dipole structure. I think we have something in common. i.e. A common graphic i use.

Fleury also discusses cortex evolution as becoming increasingly spherical as I did in my 2009 paper. Myself and Fleury have both arrived at this concept independently from each other with an entirely different approach. Fleury from studying embryology derived this from the bottom up, and myself tearing apart and meta analyzing NIH databases, trying to find the cause for lateralization of cortical function from the top down (my paper here) Whats even more interesting is that as we deducted our way into the system from either end we both ended up arriving at the conclusion of of a dipole/quadrupole interaction for most of the brains development quite independently. As of now we differ on opinions as to the type of dipole (Fleury, Electro-osmotic, Myself, Magnetohydrodynamic). Although important, these are finer points to be sorted out later. The fact is that the big picture, the dipole/quadrupole interaction is a pretty interesting convergence considering neither of our fields of interest, methods, approaches and aims are similar in any way.

(NOTE: some people still look at these graphics and try to explain the similarity away in terms of least action principle or fractals etc, however if you care to read into the work presented here further in previous posts, it will soon become clear that the "entire" radial glia which produces this does have magnetic dipole mechanisms. A more brief summary is here)

DAWKINS AND MYERS GRUDGING ADMISSIONS

In bioscience history rewards are given to those who focus on little parts so teachers deter researchers to understand entire architectures. Lee Smolin points out too much emphasis now occurs on teaching students good scientists focus on details . This small thinking method is now common. PZ Myers hatchets Fleury for a structuralist approach, Brian Goodwin passes away 4 weeks later and PZ Myers shifts his position in just 4 weeks.

"we know now that a lot of details of morphology are directly affected in subtle and not-so-subtle ways by the genetics of the organism. But I think we can also make a case that the modern molecular biological approach is also missing a significant element."

Richard Dawkins also opposed Goodwin, but still concedes. Clearly Myers and Dawkins are conflicted

"it's important that somebody like Brian Goodwin is saying that kind of thing, because it provides the other extreme, and the truth probably lies somewhere between..........It's a genuinely interesting possibility that the underlying laws of morphology allow only a certain limited range of shapes.".

1. FLEURY'S LAB FINDINGS

Fleury used mathematical techniques taken from PIV (live motion vectors in filming) to derive the conclusion that the forces he is filming in the developing brain are hydrodynamic dipole pulses, interrupted by quadrupole flow.

Anybody familiar with my work will immediately realize this is what has been predicted by dipole neurology theory. The folds in the Dipole above (A) are described by the left/right embryo dipole vectors (for motion in equation) taken from PIV trackings. As the neural folds collide a "reflection" condition occurs resulting in 4 quadrupole motions described by

from http://www.sciencedaily.com/releases/2011/08/110818101740.htm

"the mathematical formula describing magnetic fields could also be used to model fields of vectors representing the hydrodynamic flow of embryonic cells. When the two sides collided, the embryonic cells were subject to forces that can be described as those of two magnets oriented head on, which resulted in the formation of the head."

So we see a shift from dipole to quadrupole state within the early development of the chick head. This is not exactly consistent with my predictions. I would have thought there would originate a quadrupole flow initially from the ventricular zone, which had to evolve radial glia pulsing mechanisms, to overcome the earths static field. Any required amplification of the pulse to overcome the static field, leads to a fragmentation of the timing for quadrupole coherence, so the chemical gradients then switch back and forward between left/right (hemisphere top view down) assymetrical dipole vectors.

However this is early days for this area of science. Perhaps the first quadrupole was too weak for the PIV tracking to detect. I also predict that later in development the quadrupoles will develop linearity as the limbic system is composed of coherent layers. Linearising of the quadrupole can allow more complexity of the spherical harmonic type morphology proposed for the limbic system. Basically the dipole/ linear quadrupole integration for cortex / limbic system. But the situation above could be different for the chick embryo with the cortex to cerebellum size ratio being far higher than humans. I have not yet produced a clear cerebellum prediction but so far it appears to be a second but reduced dipolar/quadrupole integration. As the brain develops further (this labwork is early development) each morphological structure more fully there should be a more complex interaction of fields to represent formation of each structure.

If this is not clear what this means is the dipole neurology theory now has the following.

The predicted mechanism proven by the cortex wide pulsing of calcium waves in development (see Weissman, TA et al; 2004 in post below)

The second independent findings consistent with my 2003, 2009 structural prediction that spherical harmonics (the components of quadrupoles when linearized) dictate the form for the lateral ventricles and the limbic system (first here).

Actual live lab work illustrating in motion the hydrodynamic dipole and quadrupole are present in the developing embryonic brain at the first stages predicted. That should now eliminate those who wish to argue that the cortex dipole structure arises from a collection of least action principles or fractals.

For more information Fleury's recent paper with the labwork is here.

http://www.sciencedaily.com/releases/2011/08/110818101740.htm

2. INDEPENDENT GENETIC WORK TO DESCRIBE THE TRANSCRIPTION OF STRUCTURE

Independent work has also found the genetic basis for structuralism at the same time is consistent with Fleuries work and Cortical EMS theory.. : From Blue Whales to Earthworms, a Common Mechanism Gives Shape to Living Beings ScienceDaily (Oct. 13, 2011)

If anybody pulls the paper for this groundbreaking work "The Dynamic Architecture of Hox Gene Clusters" they will notice that the Hox gene clusters produce a linear sequence which dictates precisely the form but stops at the head. It appears that there is no sequence for forebrain development. Obviously the missing instructions are for the Radial Glia. So it is clear at this stage that evidence is not being made to fit, the predicted mechanism for the Dipole, Quadrupole formation is chemical gradients pulsing through connexin hemichannels (see below). i.e. The genetic sequence is predicted to cluster (and conserve) for this mechanism at a high level in the development hierarchy. As these models and findings just produce the head formation, none of this as of yet is providing a strong basis to support tetrapod form resulting for vortices. However that aspect has always been out-with the scope for the Dipole/Quadrupole models proposed for brain structure.

finally a nice quote from

A New Science of Qualities
A Talk With Brian Brian Goodwin [4.29.97]

"All the great scientists, Einstein, Feynman, you name them, would say intuition is the way they arrived at their basic insights, their new ways of putting parts together into coherent wholes. The famous guys are allowed to say this. The rest of us have to pretend that we're really basing everything on hard fact"

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Magnetic fields of radial glial in Prenates: Mechanisms for a cortical dipole structure by VZ calcium waves

2011-10-05T04:14:00.003+01:00

There are 6 papers in the pipeline for this as a PHD project, but this dense paper which has been peer reviewed is the best representation of the work so far.

Magnetic fields of radial glial in Prenates: Mechanisms for a cortical dipole structure by VZ calcium waves

Felix Lanzalaco (a,c), Wajid Zia. (a,b)

(a) Psychology Cares Scotland, Firdon Crescent , Glasgow G15, Scotland (b) Dept of Clinical Psychology, University of Edinburgh, Scotland (c) Open University, Milton Keynes.

http://www.dipoleneurology.org.uk/__Pages/PDF/Dipole%20Neurology%20preprint%20jan%202009.pdf

also available from NQ journal archives

You will find my meta analysis of primary cortical content in this paper. i.e.

PRESENTED TO

Integrative Approaches to Brain Complexity
October 7 - 10, 2009

Organizers:

Seth Grant, Wellcome Trust Sanger Institute, UK
Nathaniel Heintz, HHMI/The Rockerfeller University, USA
Jeffrey Noebels, Baylor College of Medicine, USA

http://www.neuroscience.cam.ac.uk/news/article.php?permalink=996f8a0687

Note that maximal ionotropic asymmetry of Sodium (Na+) gating glutamate neurons and Chloride (Cl-) gating GABA are distributed at the proposed temporal poles. Although the scans of thomspon are pinpointing NMDA receptors which gate Mg+ these postsynaptic receptors come with the standard glutamate AMPA Na+ receptor, so highest density of AMPA receptors is still at the right temporal pole. The highest density of all ionotropic neurons is at both temporal poles (exceeding frontal and parietal lobe), so if the schizophrenia image were adjusted (by fading in diagram above) for this what would be left is the the highest density of ionotropic neurons in cortex are reverse asymmetry for GABA (Na+) / GLUTAMATE (CL-) at left right temporal poles (as in lower part of diagram above) NOTE: error in image left frontal lobe not highlighted for high neuron density

I would like to make it clear, that I can build a case for the dipole model without the distributions extracted here, and that I have always been open minded to complex distributions more common in neurons. i.e there are so many ion layers that the brain works on relative gradients rather than simple positive/negative charges is basic neuroscience. From a developmental view in evolution these bilateral radial glia systems arose from sea environments, where there is an abundance of calcium (radial glia) and sodium chloride. It is interesting GABAA and AMPA receptors (the most plentifiul and ionotropic gate Cl- and Na+ into the postsynaptic membrane with equivalent but opposite voltages gradients.

Anybody familiar with the madelung constant for such salt structures will be aware just how equally balanced these ions are, even in solution. We are finding more and more evidence that these ionotropic neurons are resulting in division / multiplication scaling power laws for inhibition and excitation which are more mathematical than hebbian in structure. My idea was that for a dipoles computational function to achieve exponentiation at either pole the very basic lateralized components would require basic mathematical principles to facilitate scaling on top of each other to build coherent convergent/divergent structures that would be useful in a large scale asymmetrical sparse coding computer. Along the lines of HTM, fusters cognits and Hintons Deep belief networks except its key to bear in mind the asymmetry has to over-arch these schemes as this allows the very powerful exponential functions of minimum and maximum descriptions recruited in universal computation theory. The cortex alone perhaps may be an induction machine which is linearized by the known linearization functions of the limbic system.

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Are Cortical Magnetic models ready for experimental verification ?

2011-08-31T04:53:00.015+01:00

>>>> AUTHORS NOTE: This page provides the dipole mechanism, for evidence of neurodevelopmental dipole see HERE .<<<<<

Dipole neurology neurodevelopment theory could not have been built without existing works proposing the key mechanism that glial astrocytic cells could play in producing cortex wide magnetic fields, that is when the astrocytes are in the developmental state of a brain wide radial glia scaffold. Mainly pioneered by Marcos Banaclocha's Neuronal Activity Associated Magnetic fields (NAAMF) 2003, then expounded 2007

Following on, were increase in publications on biophysics of glia I refer to: Ingber & Nunez, 2010 , Bokkon & Banaclocha, 2010 , Pereira 2012, Størmer & Laane, 2009, Størmer et al,, 2011

Referring to the above authors, Prof Ingber of Caltech supports the proposal that glial magnetic models are a key missing link in brain function. His primary contribution is to focus on mathematical statistical models to calculate the aggregation of these proposed magnetic interactions.

His recent paper is the best physics based summary for magnetic models, and summarizes that enough theoretical work has been completed for lab verification to begin.

Pereira & Furlan provide a more neuroscience based overview for glial magnetic processes.

So the stage is already well set that Ca2+ waves can be a magneto-hydrodynamic fluid in adults. But the problem is the astrocytes are not connected across the brain for a wide range field, so only cortical column models are proposed by banachlocha. This following work provides experimental data which supports my prediction that calcium waves in radial Glial (early astrocytes) give rise to magnetic pulses across the entire cortex surface. i.e. This is a clear mechanism which can give rise to a cortex wide magnetic field in neurodevelopment.

Calcium Waves Propagate through Radial Glial Cells and Modulate Proliferation in the Developing Neocortex
Tamily A. Weissman 1, Patricio A. Riquelme 1, Lidija Ivic 2, Alexander C. Flint 2, Arnold R. Kriegstein , 1, 2, 3,

http://www.sciencedirect.com/science/article/pii/S0896627304004970
The developing brain has a stem cell derived scaffold throughout it called "radial glial" through which calcium ions pulse. This scaffold works just like the astrocytes in that it is an interconnected synctium for ions to pulse through and so macroscopic magnetic fields can arise. The mechanism is so similar that the radial glial eventually dissolves from stem cells to become astrocytes. The researchers already knew there was ion pulsing, in neurodevelopment but due to practical and ethical reasons they had to stimulate an entire hemisphere to pulse calcium ions by the method described below.

(A) Partial whole brains were prepared by removing ventral structures to open the ventricles, allowing for tissue perfusion and calcium indicator loading. Schematic shows partial whole-brain preparation and electrode placement. A, anterior; P, posterior; e, ganglionic eminence; v, ventricle; c, cortical anlage. (B) Low-magnifi cation view of Fluo-3 fl uorescence shows a mechanically stimulated calcium wave (electrode displacement of 5 μm) that propagates across the hemisphere. Higher-magnifi cation view shown in (C). Scale bar, 100 μm (B), 50 μm (C). This wave can be requested as a Supplemental Movie.

If this is not clear, what all this means is that the cortex dipole theory has a mechanism. If an entire magnetic field can pulse through each hemisphere there can be opposite poles in each hemisphere. Due to the tendency for magnetic fields to orientate the domains they influence into poles as large as the fields themselves, I predict the fields are ordering the ferroelectric components of the unmyelinated axons (microtubules and voltage gate potassium channels) into a dipole configuration.

A common objection raised to me in the past was that these glial pulses are too weak to overcome the earths static background field. After discussing this with various experts including Michael Persinger, I was informed a weak pulsed field has dynamics which can spike to overcome a more powerful static field. Lester ingber has also independently produced statistical mechanical mathematics in the paper linked above to describe this aggregation of magnetic fields in more detail than I could.

After all the controversy on the idea of a large scale quantum mind, there may still be some truth to aspects of the concept in neurodevelopment while neurons are not firing. As soon as immature neurons migrate along the scaffold and start to operate, this scaffold fades and so does the interconnected calcium ion field. Not surprising then why milk is so important for infants, but of all elements, why did nature choose calcium to produce magnetic fields? Its not hard to understand the principle.

Large magnetic fields can orientate larger biological structures together due to their ability to pass through liquids and biological material. This is one of the most highly effective ways to build a coherent structure nature could come up with. All that would be required is that developmental guidance has a means to resist these magnetic forces. The dipole neurology theory predicts that the immature ionotropic GABA (-ve chloride ion containing) and GLUTAMATE (+ve sodium ion gating) neurons are pulled apart as opposite charges in the magnetic field. This is proposed as the reason why these neurons are lateralized apart from each other at the brains temporal poles and why immature neurons have extra ion concentrations. In the paper we are reviewing the authors state

"Several neurotransmitters are present in the developing VZ at this stage and are possible candidates for a diffusible propagating signal, including glutamate, GABA (LoTurco et al., 1995), and taurine (Flint et al., 1998)"
http://www.sciencedirect.com/science/article/pii/S0896627304004970

Why then do the migrating neurons have adhesion proteins ? These proteins dont actually have a mechanism to push the neurons up the scaffold. All they can do is release hold on the glial fiber temporarily. It is predicted that the adhesion are there to resist the magnetic field from the radial Glia. Nature then has a means to control the migration by modulating the degree of adhesion strength, perhaps by degree of Astrotactin release.

"quantitation of the dynamics of movement indicate that the leading process does not "pull" the neuron, rather the neuron moves by release and reformation of the adhesion junction beneath the cell soma. The nucleus remains in the posterior, moving with the soma, and does not undergo "nuclear migration"

As to calcium ions for a magnetic field, rather than say iron. Recent work from the genome centre using blue gene computers has dated the origin of glial cells at the same time neurons become ion based and replicated into large assemblies in bilateral sea creatures. Fish mostly. At this time the most abundant element in the ocean were salt (sodium and chlorine), organic iron and deposits of calcium carbonate that older sea creatures left behind after the Ordovician–Silurian extinction event of 440–450 Ma. It’s this abundance of calcium that was responsible for creatures developing bilateral (symmetrical) hard structures and then skeletons. There is simply an abundance of the material.

Ok enough background here are the results of the lab work. In the final figure, note the increasing size of the waves, through the entire hemisphere

Figure 1.

Spontaneous Calcium Waves Propagate through the Embryonic Ventricular Zone

(A) Schematic drawing illustrates cortical anlage at embryonic day 16 (E16). LV, lateral ventricle; VZ, ventricular zone; CP, cortical plate; IZ, intermediate zone; SP, subplate; MZ, marginal zone.

(B–D) Individual spontaneous waves occurring within the VZ. (VZ is delineated at the left of each initial frame.) Waves initiate in VZ cells and propagate dorsally and medial/laterally. Scale bar, 25 μm.

(E) Selected individual cell transients (1–5) from (D) are represented as ΔF/F traces. Calcium levels in cells 4 and 5 (furthest from the initiation point) increase as levels in cells 1–3 decay.

(F) Some cells show spontaneously oscillating calcium levels, for example, cells c₁ and c₂ from (C) and cell d from (D).

(G) Spontaneous waves occur in a pattern of temporal clusters. This partially schematized image represents a three-dimensional field from an E16 coronal slice, with the ventricular surface sloping away in the lower part of the image. In this field, 11 waves were imaged in 8 min of observation. Shapes are drawn to approximate the spatial extent of each wave. The first four events (blue) occurred within the first minute, while five later events (black) were clustered within the fourth minute of observation. White events did not appear to occur within temporal clusters. Several events occurred in endfeet contacting the ventricular surface. Scale bar, 50 μm.

(H) Temporal clustering is shown in graphical form with the number of events plotted as a function of time (0.5 min bins).

Figure 7.

Radial Glial Calcium Waves May Be Involved in VZ Proliferation

(A) Cells that take up Lucifer yellow appear to be in S phase of the cell cycle. BrdU immunostain of VZ cells reveals several BrdU-positive, Lucifer yellow-positive cells. Scale bar, 20 μm.

(B) Developmental profile displaying changes in wave dynamics during the period of increasing neurogenesis in the VZ. In this three-dimensional graph, time is displayed along the x axis. Stimulated waves travel further (light gray bars) and involve more cells (dark gray bars) at later stages as shown. ATP sensitivity in the VZ also increases during this period (red bars). (E14 time point is with 100 μM, while E16 time point used 1 μM.) Spontaneous wave frequency also increases during the period of neurogenesis (blue bars). See text for numbers and error bars. (n.d., no data for this time point.)

(C) BrdU immunostain (green) with concanavalin A cellular stain (red) shows BrdU-positive VZ cells that are presumably in S phase of the cell cycle. Such images were used for quantification of proliferation experiments. Scale bar, 30 μm.

(D) In organotypic slice culture, the ATP receptor antagonist suramin (50 μM) significantly decreased VZ cell proliferation at E16 (dark gray bars), when VZ calcium waves are robust, shown as the percentage of BrdU-positive cells (normalized to control) that incorporated BrdU in S phase during a 1 hr pulse. However, at E14 (light gray bars), when waves are small and ATP sensitivity in the VZ is very low, suramin had no effect on proliferation. As expected, proliferation is decreased at both ages in the presence of cytosine arabinoside (Ara-C, 20.5 μM), an antimitotic agent.

(E) The phospholipase C activator PMT (200 ng/mL) rescues the antiproliferative effect of suramin.

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Independent model proposes spherical harmonics for lateral ventricle development (Monica K. Hurdal and Deborah A. Striegel 2009)

2011-03-21T01:29:00.009+00:00

Neurodevelopmental mathematicians Monica K. Hurdal and Deborah A. Striegel have produced a paper in September 2009 that proposes the lateral ventricles can be described in neurodevelopment by spherical harmonics. Paper is called "Chemically Based Mathematical Model for Development of Cerebral Cortical Folding Patterns"

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000524
http://www.math.fsu.edu/~mhurdal/posters/hbm10prolate36x48.pdf

The approach is clearly "structuralist"..from the paper.

One argument that has been presented against the intermediate progenitor (traditional) model is that an ‘‘elaborately choreographed set of developmental instructions [regulating the production of IP cells] would be required to account for the tremendous complexity of human cortical convolutions’’ [7]. The beauty of the GIP global intermediate progenitor (GIP) model model is that it provides an uncomplicated approach that relates to a biologically plausible mechanism of pattern formation. It uses chemical morphogens that may be governed by specific genes to control IP cell production, resulting in the ability to predict the placement and directionality of sulcal pattern formation.

Basically we see that by using the spherical harmonic for the lateral ventricle they then predict the main cortical features. This is of course consistent with the dipole neurology 2003 Limbic EMS (electromagnetic multipole solution) model that is based on spherical harmonics for the lateral ventricles as well as all limbic system appendages. But the similarity ends there. We disagree on where the prolate poles are taken from and my proposal that all the limbic morphology is the result of my proposed Magnetohydrodynamic model. I think at the current stage of knowledge a more comprehensive prediction for the correct spherical harmonics should be based on morphology and biophysical mechanisms before proceeding to mathematical modelling. i.e. to start with the approach.

Observable analysis of primary limbic /cortical system regions when mirrored and rebuilt using spherical harmonic components. The entire limbic system can be modelled from variations of spherical harmonics the components of electromagnetic multipole expansions. (a) This shows that the thalamus has a basic hourglass lobe form, and has a third ventricle between it, which is a toroid. (b) The third ventricles and caudate nucleus are mirrored produce sheared discs. Hippocampus is similar but has been left out for simplicity. These are added to thalamus third ventricle from (a). (c) the induseum griseum of the corpus callosum, a continuation of the hippocampus has toroidal structures. These are added to (a) and (b). (d) (a,b,c) are enclosed in the cortex which is a large lobe structure. (cortical surface folds left out due to modelling restrictions.) Again a toroidal structure occurs in midline. (see section 1.2) Consistent with the concept that it is only the limbic system which can be modelled in this manner the induseum griseum and cortex although possessing some of the structures (toroid’s and lobes) don’t appear to possess an overall pattern in line with any of the spherical harmonics. The indiseum griseum has four toroid’s and cortex structure overall cannot be approximated. Figure References (a Adapted from Best, B., 2009) (b Adapted from Sundsten,, 2009) (c Adapted from Nieuwenhuys et al., 1988) (d Adapted from Williams and Gluhbegovic,1980)

The point of the above exercise was to show how the limbic system when not intruded by the brainstem and skull has a spherical harmonic type morphology. If you read the rest of mypaper (chapter 3) I then try to illustrate the respective regular oscillations, delta, alpha, theta and lower beta range operate in the developed brain using a harmonic mode principle and this is a predicted mechanism and mathematical description for subconscious information sorting and pattern overlay. Its basically a fourier/wavelet like information distribution using time dependent multiplexing through axons that is predicted as the deeper method for limbic system function. i.e Importantly we are discussing here the predicted reason why its conserved in terms of information processing.

Such processing structure allows "holistic" or model free patterns to be represented in line with a Pribaum like holographic scheme. This will be scale free in terms of keeping connections in sync. i.e. Integrating spikes to tonic timings across wide regions of the brain. Its function computationally can be described in many ways. At the highest level its not hard to derive a model for creative cross associations that iterate episodic memories into autobiographic narrative across synchronized limbic areas. i.e. Imagination. This is highly powerful from a computational perspective as many cutting edge Artificial Intelligence programmers look to uncover the basis for human creativity. The Spherical harmonics processor is the part of ourselves which seeks to remain whole and integral so patterns can overlay without interference.

Wavlets spectra (linear) arise when we analyse internet communication systems, and as would be expected easily destroyed by non linear inputs. Taken from (stoev 2005,On the wavelet spectrum diagnostic for Hurst parameter estimation in the analysis of Internet traffic). It is predicted that the developed limbic system is multiplexing in this manner but the details are beyond the scope of this posting which concerns developmental morphology.

The location for the predicted multiplexing using continuous harmonic modes of Delta, Alpha, theta and low beta is predicted for thalamus, medial components of the basal ganglia and the septal (medial) end of the hippocampus (there is more on the computation in section 3 and 4 of my paper). Why exclude lateral limbic areas from this modelling ? Basically my reasoning is that where limbic components are lateral and integrate with the cortex this is where i propose a developmental boundary interaction between MHD Linear quadrupole and regular MHD dipole, and so at the boundary the structure is an integration of two different types of MHD functions which is yet to be clarified mathematically, and I would consider hard to integrate such inverse functions without computational modelling.

The first predicted harmonic models I propose for the lateral and 3rd ventricles as well as the development of the medial limbic system neuron and axon configurations that follow (staying in harmonic configuration through the first prenatal oscillations). These were chosen to accommodate the development of four ventricles but a 3 mode series may be used if we discover the cerebellum (4th ventricle) is not time synchronized with the development of 3rd and the two lateral ventricles.

To make it visually clearer how we transfer this to a 3rd ventricle.

(1 and 2 above), take in a toroidal morphology to a modeller (2 can be extended so the centre closes) then compress it as occurs in a spherical harmonic system (3), then shear and cut (3 and 4) to accommodate the fact all this evolved over the confines of jaw intrusion. From simple physical constraints as would occur in evolving systems MHD harmonic forces can transform to 3rd and lateral ventricle structures.

These would be described by colatidue and longitude, y1/3,

with the mode function of the poles, derived as a Longitudinal linear waves in the case of lateral ventricles as Y2/3 or Y1/3 for 3rd and lateral ventricle mode interactions. To simplify it would be predicted the linear waves in the following diagram, if we are looking at the ventricular zone on the sagittal plane. This linear function will then break down and fragment at its boundary before it reaches the cortex surface, which i will explain next.

BTW its should be noted deriving harmonic modes from MHD is is common for high frequencies as the application for most MHD is astrophysics,

http://iopscience.iop.org/0295-5075/94/1/10005

and this can be applied apparently to lower energy systems.

http://en.wikipedia.org/wiki/Magnetohydrodynamics

it may be necessary to use a kinetic model which properly accounts for the non-Maxwellian shape of the distribution function. However, because MHD is relatively simple and captures many of the important properties of plasma dynamics it is often qualitatively accurate and is almost invariably the first model tried.

The point is the solution offered by myself is relatively painless from a mathematical and biophysics perspective, and relies only on simple re-arrangements of spherical functions derived from Calcium ion flow through radial glia and the proliferation zones. The power of a multipole expansion is that it allows nature a relatively simple mechanism to layer associative multiplexing, by using principles of the harmonic quantum oscillator in development to emerge as a classical quantum oscillator in the developed brain.

As to the actual cortical folding itself these would be predicted not as a harmonic (as stated in the Hurdal and Streigel paper) but a boundary effect resulting from mode breakdown (at the harmonic boundary) i.e. The turing like patterns of the cortex surface I propose are magnetic dipole domains (the ising ferroelectric models for this have a similar mathematical description to turings reaction diffusion). This is where I veer of from the current accepted work in neurodevelopment. I substitute MHD functions as the overarching mechanics. However this is still compatible with previous positions based on turing models.

Figure 6: (a and c) shows thin layer ferromagnetic and fluid patterns are similar to cortical folds and turing patterns. Underneath the thin layer patterns columns form similar to cortical columns.(c) The brain veins are precisely aligned to these folds, more so than arteries. (d) As veins carry paramagnetic blood are these aligned due to magnetic forces ? (see section 2.5)

These cortical patterns (which are sometimes random) are predicted to result from an inversion of the underlying harmonic mode. My reasoning is that calcium ion flow is forced to evolve a flow which can push against the earths static field. To achieve this field strength, ion flow becomes fragmented and time independent (meaning it loses the harmonic modes) inverting itself into more powerful dipole pulses (see work by fleury above). This is (one reason) why the cortex has magnetic dipole type structure.

For skeptics who claim the magnetic dipole appearance is a co-incidence, it should be borne in mind that the cortex surface is predicted latterly (in past decade) to have magnetic mechanisms at play, as remnants of the radial glial (astrocyes) are being proposed as the missing key to where short term memory is sustainted (Ingber & Nunez, 2010 , Bokkon & Banaclocha, 2010 , Pereira & Furlan, 2010, Størmer & Laane, 2009). The key point is we are not without the required radial glia magnetic mechanism here, and by studying morphology further we can further differentiate between a cortical dipole formation which arises due to electro-osmosis or MHD force.

Some questions for the authors (They received my communication in 2009). Their mathematics involves recruiting a classic wave mechanic equation (Helmholtz) and timelimiting its function for the prolate spheroidal wave functions,

As mentioned at the end of this post here, I would advocate the use of harmonic wavefront analysis for the limbic systems development. However in this paper the explanation for how such truncation of time maps or bandpassing these functions maps out to developmental time have not been worked to remain consistent with the limbic / cortical development relationship famously mapped out to a statistical relationship across many species by Finlay / Darlington. As I predict the harmonic modes will map out in the manner i state above, perhaps future research can clarify which mathematical model is more consistent with Finlay / Darlingtons amazingly wide ranging statistical maps.

The authors will not communicate with me on this matter, so I can only guess that Hurdal and Streigel either do not accord with my approach here and/or competitive elements are at play giving rise to constraints. I mentioned to them I had been working on this since 2003 (accepted to NQ archives May 2009), while their paper states they are the first to reach this conclusion in september 2009. I have been about 50/50% successful co-operation wise with other scientists so far, which is not a bad result considering how competitive this field is.

NOTE: This model I propose here Limbic EMS and harmonics in general was never a hypothesis I had been comfortable with anyway, when it popped out the result I found in 2003. I had come in strongly from the cortex dipole end to break the brain system apart and there was nothing at all going for the concept of spherical harmonics except it appeared like a good fit. However it was important to publish this in 2009 with its problems, as the limbic system would have to remain consistent with the approach taken for Cortical EMS and the MHD models. It is more reassuring that independent work now exists coming from a more rigorous maths and developmental view which tries to build a case for Limbic models derived from spherical harmonics.

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This is not an EM mind theory.... ITS STRUCTURALISM !

2010-12-24T15:36:00.012+00:00

Central to this integrative neuroscience project the brain structures are proposed to be electromagnetic in form. And the proposal here is that intelligence has a definitive physics based form, so it might be thought this is the mother of all EM mind theories. But paradoxically this approach is not currently aiming for an EM mind theory. This post will try and explain how such a paradox could be.

Of course with something like a cortex dipole structure, it is important to be open minded to some types of magnetic brain model for various interesting concepts and developmental mechanisms, so I do mention various respected authors on this site. But for now its important just to define the physical system, and always ask if conventional concepts can fill in the rest. Overall I stick with traditional approaches and do not get into quantum mind theories. For the following reasons.

The concept of quantum mind is over-rated and takes us off track. It has been popular because theorists were fusing “cool” topics like Einstein and Darwin, and many academics wanted to be the first to claim this territory. And for others it has been the “soul” or escaping into the mysticism of quantum physics. But what structure of computation would a quantum mind even bring anyway ? Surprisingly many EM mind theories don’t even explore or formalize this basic aspect ! Now that we are actually building quantum computers its a a lot clearer what that would be.

What quantum computation really brings us, is dense matrix connectivity, something we already simulate with digital matrix systems. Which is why the Dwave had to scale up its system to prove its advantage over digital.

Findings of the entire set of Quantum properties in classical fluids that show us fluid dynamics can solve quantum weirdness in terms of pilot waves. So it appears like wave/particle could be unraveled and there may be no quantum/classical divide after all. You would think this would be interesting, but there is often a blanket denial of this information when raised in a discussion. Such denial serves to illustrate there is more of an attraction to the mysticism of the quantum ideas rather than a desire to get to the substance of solving the problem. i.e. What is wave particle duality really about ?

Yves couder in his fluid dynamics lab in CNRS Paris. His team have created the quantum phenomena by slow motion filming the motion of fluid droplets after they are oscillated. We uncover the same patterns for entanglement, and many other quantum phenomena. These accord with De Broglies Pilot wave concept. Put simply the waves guide the particle, so they are bound together.

Its important to point out what an EM mind theory is. For example the traditional neuron is defined by a combination of EM fluid equations, but the computation is still defined by traditional logic. Although even that is contested in neuroscience, however most neuroscientists do not subscribe to a full EM mind theory which would be a quantum mind theory, popularized to an extreme by Stuart hameroff. He proposed most neurons operate together and compute via EM fields orchestrated across the brain via the inner neuron structure (microtubules). The concept raised many interesting issues but did not survive scientific testing and was weakened by the arrival of more complete brain mapping projects. We now have new information such as that of dendritic computation, which places the logical process of neurons in the dendritic webs.

Where we now are sure computation emerges. From dendrites (1) to population burst patterns (2) to entire brain regions locked together in phases (3). We could represent these phase locks to have similar patterns as the quantum interference patterns, but we know they are classical patterns. So the same phenomena can emerge by different means.

What is important to bear in mind about the dipole cortex concept is that it does not seek to replace or do away with current neuroscience. Its more of an integrative approach to piece the brain together into a larger picture. For example although it proposes an EM structure for the brain, its not real time EM action like a quantum mind. A quantum mind theory would propose magnitudes faster neural processing than the millisecond of the action potential.

In biology there are many interacting levels and different types of physical principles. Physics in coded and controlled by biology, so a biophysics of brain structure needs to represent all these (and more) different levels within the same picture. In the brain structure the radial glia which produce the magnetic fields pulse these fields when migration is required. Then stop while other genetic or biophysical processes are in operation. So like conventional neuroscience, the solution will not be simple concepts such as a prevailing magnetic field model for the brain.

In neurons electromagnetic fluids are recruited and controlled by genetics, to the point that every aspect of how physical principles operate is slowed down in time, so they can work together within the limiting frameworks of how they are built, maintained and can reproduce their functions. All this becomes so complicated that the action potential cannot transfer into the speed of a pure electromagnetic field as it has far too many sub aspects inside to build, produce and maintain it. All the same applies to the proposal presented here for the brains EM structure, so it is entirely consistent with current neuroscience

This is just some of the complexity at the synapse in terms of protein signalling, and represents only a fraction of the aspects shown. For example if we were to fill in everything we know about the membrane it could not fit on this graphic. Same for the electrophysiology, genetics, neuroimmunity, Glial cells, Plasticity, Signal responses and much more.

More specific problems with quantum or even magnetic mind theory

I have to remain open minded, but so far there does not yet appear as if there is any "real time" EM role in actual adult processing. At least nothing that could threaten current models. So for computation this project develops with traditional neuron/structure approaches, and then considers as an adjunct any EM aspects mainstream (not fringe) neuroscience has found. For example it is already accepted in science that magnetic structures have a variety of computational properties so from that we can consider what could a magnetic field contribute to information processing when it assists in and is integrated into the development of complex computational structures.

Magnetic principles do not really scale up it appears. If they are coherent in development due to radial glia (which dissolves early in development) then this means loss of this means for a widescale magnetic mechanism in the adult brain.

The first problem is that we are not sure if there is any large scale field in the brain that arises from individual magnetic domains, because MEG readings are taken at the population level, and the equipment to measure individual axons while still active in the larger field is only just being developed.

The second problem is the magnetic mechanism (radial glia Ca2+ in connexins) mostly fades after cortical development and leaves the glial system. So for this approach proposed here, I don’t have a mechanism for the developed brain. At least not in the scale required to fit the concept of a magnetic multipole system.

The third problem is neurons are operating at sub-electromagnetic speed. They are evolved for the millisecond range. And why would brain networks even require such a field, when there is already all the connective machinery in place anyway ? Its already explained well enough. The outstanding questions in neuroscience (like how coding works) are not illuminated by current QM mind theories.

The biggest problem for EM mind ideas is the emergence of most ordered complexity at the microscale, and the way in which biology controls/encapsulates/integrates many difference principles of physics

And more ! see this for criticism of quantum mind concepts

http://www.scholarpedia.org/article/Field_theories_of_consciousness#The_electromagnetic_field_theory_of_consciousness/

If there is no EM mind theory, then why would there be an EM structure ?

The ideas presented here are more guiding concepts to understand organizing principles in neurodevelopment. Currently this project is not looking to extract from these concepts any computing with continuous EM coherence or magnetic organizing effects at the atomic scale. All we need to derive is the overall structure of computation that EM fields might leave in their imprint. But still remain partly open to the possibility of some EM aspects we don’t know off.

Some brief examples of how electromagnetism can assist in the development of good computing structures. Magnetic fields can assist the coding of genetics to ensure microtubules fall in line with a soliton axon model, and these are all bundled in a neat linear manner suitable for good volume synchronized coherence of information transmission. This property is called flux, or magnetic ropes. Magnetic clustering (right in diagram) is actually used as an algorithmic method for sorting information in computer science. The magnetic ordering induces very good hierarchical structures.

So in the actual cortical structure it is proposed that the structure provided by magnetic fields to the cortical asymmetry, would provide impart different types of tree structures that when filled with neurons have good structural properties for feature extraction. Magnetic fields are a very useful tool for nature to get cheap and controllable computing power which scales up well to increasing complexity. Neurons evolved first, then brain structures evolved to use magnetic fields to order increasing amounts of neurons with some very good structural computational properties.

However with our current knowledge it can also be proposed that even if EM fields stop in development, they will have imparted a computing structure which leaves an imprint of the essence of general computational properties. This “essence” evolved to integrate nicely with being filled by the brute force numbers of neuron sub-components. So nicely all that has to happen is the structure develops/crams as many neurons into the computing structure, then only has to prune these out or form some more later in limited numbers.

The process of how to build a structure can be itself tied into how the structure itself operates. The physical constraints of the building process if self evolving (rather than designed) would naturally ensure this takes place

What general computational properties or “essence” would these EM fields be ? This is another part of the project in itself, but it would basically be a simple dual process computational system which arises from fundamental physical properties. Most AGi theories (with good test results) are dual process theories and can be reduced to dual processes in physics. Googles Deepmind (the Atari beater) is basically dual processes of action-perception.

Googles Deepmind system is based on a simple dual process computing. They use Deep-reenforcement. Dual process underlies even human cognition (see wiki summary here). If dual process underlies cognition it is reflected in brain structures (see wiki summary here). I have written an unfinished AGi paper (2012) which used dipole neurology and dual process principles to predict what Deepmind would require, even before it was built !(see here). My later 2014 paper is actually a dual process AGi concept, but applied primarily to physics and neuroscience.

The basic concepts behind Googles Deepmind general learning can be expressed in a few elegant equations based on universal induction, and these can in turn be expressed in fundamental physics. But if you provide these equations life by dressing their operational structure with a larger brute force numbers of computational elements, they will in principle become complex general learners, even if the computational elements vary a lot. See wolframs principle of computational equivalence for more on this. Or simpler put, the structure of a mechanical machine can still operate when made out of many different types of elements.

Stephen wolfram filmed explain the principle of computational equivalence (see video here). This principle is based on a large project which used computers to layout the landscape of proof systems (entire book here). At this stage its an idea not to take his approach as proven for the landscape of all complexity, but it is interesting for the more well defined system of the brain.

SUMMARY

Because quantum computers do compute, It was always important to be open minded there could be EM effects. I have done so in this project and gathered the best I can find together. Those remain part of the project. And the concepts from this project, such as feature extraction resulting from converging/diverging networks derived from magnetic poles is an EM derived computation. But if there is no real time EM field, then its also a classical computation. It is also very important to ask what quantum computers do to information physically and can this occur by other means. Or is there even an EM/classical divide after all ? I think there are probably many ways to produce similar dual process types of computation at different levels of complexity. EM fields evolved as they enable a simple and cheap way for nature to get us a good dual process computing structure that we can shoehorn computational elements into via chemotaxis (neuron guidance).

So this is why this is not an EM mind theory as currently known. It is more suspected the actual highest level computing scheme to be derived for the brain will be an adjunct framework which is consistent with current neuron population models or biophysics concepts such as walter freemans mass action. The computing derivation is still a work in progress, and that is why I publish on AGi. To compare how universal computing matches with neuroscience. My 2014 paper started from basic thermodynamics (as a foundation). A recent post here outlines some broad themes in the meanwhile.

Easy read poster of the theory

2010-02-10T00:49:00.007+00:00

>>>> AUTHORS NOTE: This section is still a bit of a mess .<<<<<

WHATS THIS ALL ABOUT ?

This website summarizes the progress of controversial "Dipole Neurology" theory (summarized here) which has exceeded my original expectations by developing from a spare time academic project (in neuropsychology) and has now moved into the search for a common physical pattern/topology for intelligence/consciousness (starting from the mammalian brain). It is also developmental biology with accruing evidence backing up the basic concept. If basic becomes robustly proven, I expect this to become a strong contender in the top 10 of integrative neuroscience theories.

The idea is controversial more by its scope and freshness, rather than proposing any quantum mind theory. The concept is proposing that the brain has a simple overarching structural framework (entire corticolimbic system) which is itself of a simple physical/computational form. This structural form is not the hodge podge of evolution neuroscience currently thinks, and is coherent enough to actually drive how the small scale computational elements (neurons/axons) etc end up filling up the large scale form for the overall highest level computation. The Cortical Dipole structure itself is proposed to enable feature extraction, as this works by brute force elements (neurons) operating through dipole laterality in a conceptually simple but well connected topology. This structure arises in neurodevelopment to assist in guidance mechanics and is partly MHD (magnetohydrodynamic) in origin. From the neurodevelopment angle this concept is improving with new evidence from labs out there every year.

I have to remain open minded, but so far there does not yet appear as if there is any "real time" EM (electromagnetic) role in actual adult processing. At least nothing that could threaten current models. So for computation this project develops with traditional neuron/structure approaches, and then considers as an adjunct any EM aspects mainstream (not fringe) neuroscience has found. For example it is already accepted in science that magnetic structures have a variety of computational properties so from that we can consider what could a magnetic field contribute to information processing when it assists in and is integrated into the development of complex computational structures.

This physics is important, because if computation is simple EM/thermodynamic both within and throughout the entire brains physical encapsulation then if true, this is a massive theory for neuroscience. But as i repeatedly will state, I don't actually explore the EM side too much in this project. The real controversy of this work is that it challenges the current idea that no topdown structural computational properties should be derived from physics and clearly assigned in general to the cortex or limbic system at the actual full structural level.

If that were not enough, this concept produces an inescapable finding or presumption (to be falsified). This is that if intelligence/consciousness comes from simple physics and gives rise to this specific type of dual process thermodynamic/EM physical structure I propose the brain has, then by logical conclusion this structure might be roughly applicable to the successful operation of most general learning systems where our laws of physics apply. So If this project continues as it has, it is moving to being able to make future testable predictions for the viability of Artificial General Intelligence (AGi) systems. That is whether AGi will have to possess something like a brainlike physical form as their complexity increases. Primarily due to constraints by the laws of physics.

Many science futurists predict that from 30 years from now, most primary technologies and later down the line, the core of most life itself will be reliant on complete AGi and mind understanding. My approach also provides a pioneers view of the brain which gives many original ideas. So its pretty exciting to be able to explore and lead these pioneering areas at such an early stage. Its also for this reason I had to become independent, but I kind of enjoy being a maverick now anyway :) I guess the only big question for newcomers here, is whether the cortex has dipole structure. Well first ask "what else could it be ?"

Check this beginners video for a quick visual intro, or open the poster further down

For this project I usually work alone as a new insight (or self criticism) comes to mind. As I am a student trying to develop this very complex work to the level of clarification required for laboratory testing, PhD and other applications I co-author peer reviewed publications with academics senior to myself. I also expose myself to group discussions and when affordable will present at a major neuroscience conferences with the aim of soliciting high quality criticism.

To explain the concepts I often generate simplified physical models for the cortico-limbic system as this appears to be the brains “information engine”, but do not take this to mean that other complex facts are ignored. Look at the cortico-limbic models here as the final overall "computational superstructures" in the same way we look at cortical columns as mini-computers. My most recent co-authored published application of physics to this simplified approach (see 2014 paper in links) tested whether fundamental thermodynamics is consistent with computational principles from entire structural morphologies.

The theories history summarized on this website will show a sequence of specific predictions has progressively lead from development of hypothesis to actual current theoretical framework. My proposal is that to develop and innovate a well worked physical topology of intelligence will be pivotal. Complex self developing AGi will eventually have to self organize into an optimal physical topology and I propose it will not be too different from the simplified form presented here.

See this blog post for a brief introduction - my 2013 paper for the first draft formalization of computational principles and my 2009 paper for the background neuro model. The next work planned will derive specific key facets, such as re-enforcement learning, spatiotemporal perception and self-awareness from the current simplified thermodynamic basis.

NOTES ON THE WHOLE QUANTUM MIND THING:

Although the brain structures are shown to be EM in nature, and the proposal here is the intelligence has a definite physical form it might be thought this is the grandad of all EM mind theories. But paradoxially this is not a hameroff style quantum mind theory. Or even like other EM mind theories. Although i have been open minded to some types of magnetic brain model for various interesting concepts and developmental mechanisms, so I do mention various respected authors on this site. But overall, I stick with traditional approaches and do not get into quantum mind theories, partly because i dont think they are as interesting as stuart hameroff et al., seem to think. The concept of quantum mind is over-rated, and appear to tempt people into various non-science thinking styles that are way off track from computation and more about imagination, desires to remain in obscure thinking or understandable drives to lead others through spirituality than solving problems or understanding neuroscience.

By contrast taking a "so what" attitude to QM mind, and concentrating on deriving a more boring thermodynamic formalization of this approach can steer us from those thinking pitfalls. This leads me to the conclusion we can have QM "style" processes in classical scales which we already perform on current digital systems as matrix problems. Also from the physics side quantum computing, there are recent findings of QM entanglement in classical fluids that show us fluid dynamics can solve quantum weirdness in terms of pilot waves. So it appears like wave/particle can be no big mystery. i.e. no quantum/classical divide after all. It seems getting into quantum mind is way off track if we dont need quantum computers to do what the brain does anyway.

This poster here was designed for hitting you with the theory in a get it quick visual manner. If you wish data, and more facets of this complex theory in depth there are the papers. And please not this theory is not derived from images and never was. Although even if it was that should not be a problem For this poster, Click here for full size (then click again). Cortex in centre is mirrored on coronal plane of the human brain to clarify the dipole structure. The text underneath this image helps clarify the posters points.

In this poster I hope to illustrate that the strongest model for all of these cortex features to occur together in on developmental timeframe is a magnetohydrodynamic field model, which would assist in axon guidance and neuron migration for these larger brain structures. If you read the newer posts on this site you will find evidence is accumulating independently to verify that the astroglia which produce cortex structure have a magnetic field mechanism.

In response to peer review, it should be stated the cortex dipole has all the features particular to magnetic or ferro-electric dipoles. All these features cannot result from entirely electric dipoles (according to physics known so far). Also bear in mind all EM structure similarity is not actual live EM. It is what occurs when EM forces in neurodevelopment are controlled, frozen and encapsulated within conventional biological details.

* The axons have bent into a very clear magnetic dipole configuration; this would require a magnetic field that can pass through the biological tissue without interference. Corpus callosum and other axons in development are unmyelinated and the remaining axon components are ferroelectric i.e. microtubules and voltage gated channels (refer to H. Richard Leuchtag). This means it is possible to propose axons are able to be influenced to move in any magnetic dipole or linear MHD field present in the radial glia.

* There is a broad domain wall at the longitudinal fissure. These domain walls are only known so far to result from magnetic or ferro-electric forces. i.e. Magnetic forces. I remain open minded if anybody finds domain walls dominated by the electric dipole, however as far we know virtual photon forces due to bound magnetic fields are the mechanism for domain walls.

* The cortex gyri and sulci as well as the cerebellum (not in image), have appearance of variations of magnetic domain patterns (i.e. magnetic stripe domains). These patterns have correct scaling in accordance with neuron to glia ratios in various species (right of diagram). Variances in astroglia correlate to Variance in magnetic field strength (see mechanisms below).

* The cortical columns themselves have magnetic pinwheel type structures in the entire morphology at the boundaries and center. Similar pinwheels have been found in the ventricular zone radial glia by other researchers, so we have a facility to propose the internal formation of cortex columns by a MHD mechanism which is consistent from bottom to top layer of their developmental timeline.

* Axons have an MHD soliton structure, and we know now axons are EM solitons. Neurons are not so easily defined in EM morphology terms (except looking like electrostatic branches) but they do follow the "greedy growth" principle which can be derived as least action which occurs at magnetic poles

* To summarize in case this is not clear, every single part of large scale cortical morphology has a magnetic type feature, with a strong case for the mechanics in progress.

However it should be pointed out that traditional theories of brain structure and development exist without an EM framework. i.e. Tension based, because it is possible for that to occur. If we are being completely objective and entertaining what I say it is possible to say that any resemblance to magnetic structures are a collection of interesting co-incidences. However developmental frameworks are currently incomplete and new works point to dipole forces being present.

Dipole neurology also has a specific mechanism defined to make sense of confusion regarding chemoattractants and adhesion systems in development (see menu bar - neurodevelopment). Dipole neurology also provides computational frameworks for general computing which are also on track to make sense of our inability to define feature extraction in terms of neural coding. So with one physical approach neurodevelopment and computation become unified. So, If this theory is correct its a very big theory for neuroscience.

MAGNETIC MECHANISM: There is recent acceleration of growing work for large neuronal activity magnetic fields to be produced by the mechanism of the astroglia ion flow i.e. Neuronal Activity Associated Magnetic Fields (NAAMF) proposed by Marcos Banachlocha in 2001. More recently several others researchers are moving into this field. Lester Ingber a former Fenyman student who provides the statistical mathematics with Paul Nunez (a founding figure in EEG) have produced papers in this regard. Other Recent examples are Bokkon & Banaclocha, 2010 , Pereira & Furlan, 2010, Størmer & Laane, 2009, 2011. I Estimate there may be about 25 papers since 2001 on this area with insistence for labwork grants on the most recent papers. However the problem is even though the biophysics and maths have now been well produced by these researchers, it is very technically difficult to do such research on adult brains (due to magnetic interference) without building a specialized lab. (summary of these papers here)

ARRIVAL OF EVIDENCE IN NEURODEVELOPMENT : However for the purpose of neurodevelopment the news is more positive. The radial glia calcium ion flow proposed to give rise to the brain structures is actually just using the above NAAMF mechanisms, as the astroglia in adults are the remnants of the radial glia in neurodevelopment. A study on development (Weissman, TA et al; 2004.) has found the calcium ions can pulse through the entire hemisphere radial glia in development. This would mean the NAAMF magnetic mechanisms can in theory apply to the entire cortex. (see more on that here). More recently last year the developmental biophysicist Vincent Fluery independently films a hydrodynamic dipole and quadrupole force dominating the entire development of the chick embryos skull. This hypothesis is not then without a strong starting basis for evidence on several levels. I cannot reveal details without permission but there is labwork planned to measure the brains field strength, pulses in times with the observed dipole /quadrupole hydrodynamic flow, so linking physical observation with underlying physics.

From V. Fleury, 2011. A change in boundary conditions induces a discontinuity of tissue flow in chicken embryos and the formation of the cephalic fold Eur. Phys. J. E (2011) 34: 73

And more papers I was not aware off. Apparently there about a dozen or more papers studying the presence of an EM Field in neurodevelopment. Many of these papers propose that the axons are evolved to conform to an EM field. More of that when this site is updated. For now read the references in this paper.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566847/

Text from above paper for the image below

"There are numerous hypothesized mechanisms by which cells might sense an electrical field. A weak electrical field could impose a force on negatively charged cell surface receptors, or alternatively the electric force imposed on positive ions (Na+) could result in the flow of their associated hydration shell, which exerts a drag force on cell surface membranes. The resulting asymmetrical redistribution of cell surface receptors, such as the ones involved in sensing chemokines or motogens, could affect cell migration. Alternatively, the electric field could conceivably trigger voltage-gated ion channels or exert forces on adhesion receptors, such as integrins, which result in asymmetrical binding to extracellular matrix (ECM) proteins. Finally, phosphatases, such as Ci-VSP or PTEN, mediate cellular responses to electric fields"

What next ?

When you consider that if what I propose is true the ramifications for neuroscience are there. After all its not just another detail or facet here. Its the entire framework. However its still a backwater project, primarily because firstly that's the only way to develop this. Its not very likely i could develop all these various facets in today's quick results, corporate science career framework.

So this work itself represents my slow DIY neuroscience education through university part time with the publications being prepped for PhD. This process which demands i produce 3-6 good publications, should get it into the kind of shape to be more usable to apply to neuroscience.

In the meantime a new area of research has opened up which was predicted by this research. That of EM fields in neurodevelopment. Summary here called

"The bioelectric code: An ancient computational medium for dynamic control of growth and form"

https://www.sciencedirect.com/science/article/pii/S0303264717302848

When I get time I will curate the dipole mind project with more skill. At the moment I am prioritizing on university lab work as well as a commercial mind improvement program. Some details here

http://projectzeromind.blogspot.co.uk/

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Paper now available from NeuroQuantology Archives

2009-10-20T16:27:00.010+01:00

A current version of the theory which is for sure becoming a theory of neurodevelopment and a tight integration of evolution with physics is available at Neuroquantology archives

After the conference "integrative approaches to brain complexity" it was realised that the dipole concept of separation of charges may reach back 700,000,000 years when bilateral (symmetric) nervous systems and ionotropic GABA/Glutamate receptors formed in salt water environments. Although it is still do be determined if this a co incidence and the symmetry has nothing to do with this..

The poster presented at the conference is an easy read version of the theory also available here.

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Integrative Approaches to Brain Complexity conference October 7 - 10, 2009

2009-07-18T01:59:00.012+01:00

Due to travel logistics TSC 2009 was not a good direction for progress of the Dipole Neurology project. I had concerns about taking the theory to a philosophy biased neuroscience conference when there are many technical problems still needing to be ironed out..

We feel the best direction is the Cold Spring Harbor Laboratory/Wellcome Trust conference on Integrative Approaches to Brain Complexity. This will be held at the Wellcome Trust Conference Centre on the Wellcome Trust Genome Campus in Hinxton, UK. We will ask to present under "Data integration and systems biology".

From their site

"Understanding the structural and functional complexity of the vertebrate nervous system is the theme of this meeting. Advances in genomics and proteomics are defining the molecular building blocks that underpin the structural complexity of the brain and it behavioural output. Combining genetic discoveries with anatomical, electrophysiological and behavioural findings, it is now feasible to integrate this knowledge."

Photos of the conference here

What was not expected that was Seth Grant the organizer had a project which had used the Sanger institutes Blue Gene computers to crunch the origin of synapses back through as many species as possible over nearly a million years. Basically what we find is that we can date the start of axons and neurons as we know them to about 766 Mya (million years ago). Research by Hartline (2011) finds that evolution of glial cells into their current form is a more recent development around 500Mya (approx) and in particular Astrocytes and radial glia converge with the arrival of Craniata, species with hard skulls and the development of brain structures as we currently know them.

The very concept of the dipole neurology approach is that radial glia and the flow of ions through it allows for the emergence of large scale structures which are governed by the natural laws of Magnetohydrodynamic force, and that these structures have inherent computational functionality by coherently organizing neurons and axons within a magnetic field structure. If anybody is familiar with my work they will understand I propose the MHD structures of dipoles can facilitate asymmetrical feature extraction and those of spherical harmonics (limbic system) cross association connectivity in networks similar (only in processing style) to those sought after in adiabatic computing. The integration of both these basic top down quantum level functions at certain key areas in the brain produces more processing complexity (hippocampus, caudate, thalmocortical loop) as long is this evolution occurs in tandem with the bottom up components of axons and neurons. From here we have a highly powerful multi-level system which can then give rise to more complex computational functionality and this is why the evolution of brain development has facilitated greater ratios for glial to neurons as a consistent factor in evolution.

image above of the clade craniata from http://www.fishbase.org/manual/orders.htm

For these components to evolve together to produce computational function requires at some stage in neurodevelopment they are coherent with each others mutations at the biophysical level, such that axons and neurons can be influenced by the top down glial structure, while the radial glial structure also has to remain plastic to small scale changes within the system by shrinking itself down after development just to areas such as the cortical astrocytes, thalamic reticular nucleus and ventricular neurogenesis areas. If the radial glial were to remain the brain would forever be dominated by top down large scale developmental reformation.

By contrast previous to this development of large scale structure by glial grown structures, neurons and axons were useful but ad hoc components in creatures, which never organized much of great significance, by comparison to recent brain development. Grant does finds increased duplication of neurons around the time of the arrival of astrocytes, but it is not stated this is linked to glial proteins as these were not part of his 2009 study. From the perspective proposed here neurons and axons are not really interesting from a computational aspect but turn out to be bit players in a multi-level evolution where the structures facilitated to develop by glial cells are the more recent dominant factors in the development of intelligence (of course social organization and our interaction with technology has overtaken this by magnitudes).

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Paper has been updated in light of trial peer reviews

2009-05-23T08:31:00.006+01:00

The paper downloadable from papers page

has been updated and reformatted to journal standard. Still not quite finished yet. Hemodynamic shift has now been removed, but a section on neurodevelopment of the cortical structure has been added (section 2.6).

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Toward a Science of Consciousness Conference 2009

2009-04-23T18:31:00.009+01:00

A poster presentation of the theory will be viewable at TSC 2009.

Toward a Science of Consciousness Conference conference which will be held in Hong Kong from June 11th to 14th, 2009

http://www.asiaconsciousness.org

Due to pressure on time and resources i am not sure if i can make it personally. The poster will be up at poster session 1 in Physical & Biological Science section

http://www.sd.polyu.edu.hk/tsc09/posters1.html

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Paper updated.

2009-03-14T18:11:00.005+00:00

After consulting with other scientists in the field of neuromagnetics the main papers section two, has been updated to include a previously ditched concept. That the hemodynamic response detectable in BOLD (Blood Oxygen Level Dependent) mri could be involved in sustaining cortical activity patterns.

Updated paper here

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Progress after journal presubmission.

2009-02-23T13:42:00.014+00:00

Feedback from journal editors has been favourable with positive feedback.

To summarize.

1. A technical version of this work has the go ahead for submission to a high impact journal. Another journal is agreeable on the idea of a follow up paper. An evolution based interpretation of the theory, which will be kind of a popular psychology version of the theory.

3. Got the OK to give a poster presentation at this years "Science of Consciousness" festival in Hong Kong. This will be in June 5-15th 2009. Quite a high profile event. More about that here

http://www.asiaconsciousness.org/home.html

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Main site now located at www.dipoleneurology.org.uk.

2009-01-24T20:05:00.009+00:00

Site testing is complete. Main site now located at

http://www.dipoleneurology.org.uk

All papers are located there, as well as online fully referenced versions of the theory.

Preprint of paper

Introduction Video

Online version of theory

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Theory summary

2008-10-19T17:15:00.005+01:00

First a quick summary of the theory.

Dipole Neurology:

1. Accounts for the distribution of all currently discovered lateralization of cortical neuronal content. Most neurotransmitters, receptors, modulators, enzymes, lipids, and secondary messengers.

2. Accounts for and unifies the marked difference between limbic system and cortical processes as discovered so far by EEG and computational analysis.

3. Explains why the cortex and limbic system have the structure they do.

4. Already has an insight to offer into brain wide disorders like schizophrenia

5. Unifies more neuroscience theories than any other known model.

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Testing blog

2008-10-17T14:02:00.001+01:00

Testing blog feed