SCLin's neuroscience blog

Dopamine neurons don't just encode valence -- Some encode salience

2009-05-31T14:37:00.009-04:00

Two types of dopamine neuron distinctly convey positive and negative motivational signals

Nature advance online publication 17 May 2009.

Masayuki Matsumoto & Okihide Hikosaka

This is the classical kind of (putative) DA neurons, which show opposite responses to reward and punishment, thus encoding the valence of a stimulus.

This classical picture is well and good. But not so fast...

Mastumoto and Hikosaka here show that there is in fact another group of (putative) DA neurons, which have similar bursting responses to both reward and punishment. Thus, it appears that these DA neurons are encoding the salience of a stimulus, reminiscent of how basal forebrain neurons encode salience.

Hippocampal theta oscillations are travelling waves

2009-05-31T14:22:00.014-04:00

Evgueniy V. Lubenov & Athanassios G. Siapas

Nature 459, 534-539 (28 May 2009)

A movie is worth a thousand words. This is cool!

Define anatomic boundary by large scale expression pattern analysis

2009-01-19T11:10:00.003-05:00

Genomic Anatomy of the Hippocampus
Thompson et al, Volume 60, Issue 6, 26 December 2008, Pages 1010-1021 -- Neuron

What features really define the boundaries of neural systems? Anatomical landmarks and morphology have long been the guide, but these criteria come with obvious caveats that, at the transition zone where one region morphs into another, the determination of the exact boundary may be somewhat arbitrary. Is there an abrupt transition? or graded transition? and are there hidden subdivisions?

The authors here provides novel insight on this issue using genome scale gene expression data. Thompson et al applied powerful statistical pattern recognition tools to determine functional domains within the hippocampus based on the spatial expression pattern of ~3000 genes. This analysis not only recovers the major divisions of the hippocampus - DG, CA3, CA1 - but also uncovers subdivisions within. I really like this data-driven approach, which relies on the same tools that are used to uncover patterns in neurophysiological data.

Many molecules that define the map are adhesion molecules, which are important for forming neuronal circuits and finding projection targets. Supporting this idea, the subdivisions described in this paper (lower row) correspond to the spatial divisions in several previous reports studying the input-output relations of the hippocampus with other regions (upper row). Thus, this approach provides the molecular underpinning for these subdivisions and identifies molecular targets that can uniquely specify a functional domain. On a broader perspective, the same approach can be applied to all brain regions and shed new light on the functional organization of the brain.

Tracking of State Value in the Amygdala

2008-11-10T17:22:00.002-05:00

Moment-to-Moment Tracking of State Value in the Amygdala
Belova et al. 28 (40): 10023 -- Journal of Neuroscience

The amygdala is important for the regulation of emotion and also in learning. Neurons in the amygdala changes their response properties during learning. But what exactly are the amygdala neurons doing? what are they encoding? what is their computational role?

Previous studies have establish that amygdala neurons encode the motivational value of the associated outcome. When animals learn that a cue CS is associated with an outcome US, a subset of amygdala neurons show stronger responses to CSs that are paired with rewarding USs (positive value coding, example neuron A at 1-2 sec). Conversely, another subset of neurons code negative value and respond more vigorously to CSs paired with punishment (like airpuff, example neuron B at 2-3 sec).

The current study extends these previously findings to suggest that these amygdala neurons in fact code the value of the state in the generic sense. In essence, what the authors found was that neurons encode value not only for the CS, but also for other behavioral epochs, including the fixation point and also to the US. In the above examples, neuron A prefered rewarding CS and also showed stronger response to the fixation point, while neuron B preferred aversive CS and reduced its firing rate to the fixation point. This pattern is generally true for the population (see D).

I think this paper is really powerful because the conclusion is based not only on one behavioral epoch but on all the epochs. This comes much closer to understanding the roles of these amygdala neurons, which is to keep track of the current state value. Since neurons are active all the time and in various behavioral contexts, a thourough understanding of their physiological/behavioral roles require considering all these scenarios, not just any particular one.

This paper is also important because the coding of state value has important implications in computational theories of learning.

Neural correlate of decision confidence and uncertainty

2008-09-01T12:25:00.006-04:00

Neural correlates, computation and behavioural impact of decision confidence

Adam Kepecs1, Naoshige Uchida1,2, Hatim A. Zariwala1,3 & Zachary F. Mainen1,4

Humans and other animals must often make decisions on the basis of imperfect evidence^1,². Statisticians use measures such as P values to assign degrees of confidence to propositions, but little is known about how the brain computes confidence estimates about decisions. We explored this issue using behavioural analysis and neural recordings in rats in combination with computational modelling. Subjects were trained to perform an odour categorization task that allowed decision confidence to be manipulated by varying the distance of the test stimulus to the category boundary. To understand how confidence could be computed along with the choice itself, using standard models of decision-making^3,^4,^5,⁶, we defined a simple measure that quantified the quality of the evidence contributing to a particular decision. Here we show that the firing rates of many single neurons in the orbitofrontal cortex match closely to the predictions of confidence models and cannot be readily explained by alternative mechanisms, such as learning stimulus–outcome associations^7,^8,^9,¹⁰. Moreover, when tested using a delayed reward version of the task, we found that rats' willingness to wait for rewards increased with confidence, as predicted by the theoretical model. These results indicate that confidence estimates, previously suggested to require 'metacognition'^11,¹² and conscious awareness^13,¹⁴, are available even in the rodent brain, can be computed with relatively simple operations, and can drive adaptive behaviour. We suggest that confidence estimation may be a fundamental and ubiquitous component of decision-making.

This is a really nice paper by Adam Kepecs et al from CSHL. The gist of the paper is in the following figure. The model (a-d) illustrates the perceptual decision process of determining which category the odorant stimulus (s) belongs to, by comparing the stimulus (si) to the memory of the boundary (bi), each drawn from its respective distribution. This leads to the determination of the perceptual category (si>bi or si less than bi) and the decision confidence (absolute difference between si and bi). The behavior of the decision confidence construct (d) is matched perfectly with neuronal responses in the OFC (e, g).

One important concern of the authors' interpretation is whether the OFC neuronal activity signals uncertainty or an error signal. This is nicely addressed in the method section:

The observed selectivity of neural activity for the upcoming outcome might arise if, after executing a choice, extra sensory or memory information enters decision-making circuits and causes the realization that an error occurred even before obtaining feedback. According to this interpretation the negative outcome selective population of OFC neurons would signal error⁴⁴ instead of uncertainty. In contrast, the highest observed firing rates were associated with near chance level performance and not errors (Fig. 4g, f). To test this more rigorously, we asked whether an ideal observer could obtain better performance than the experimental subject if it could switch choices based on the firing rate after the choice and before feedback is provided. In all but one negative outcome selective neuron (1/133), the highest firing rates (top 5% of trials) were associated with chance level performance (within the 95% confidence interval). Therefore negative outcome selectivity does not imply that OFC neurons are actually able to predict error trials but rather that high firing rates predict near chance level performance consistent with an uncertainty signal.

The discovery of this neural correlate of decision confidence opens many new research questions. How is uncertainty/confidence calculated in the neural circuits? What are the influences of decision confidence on behavioral responses, learning and memory, and attention? No doubt these questions will be further explored by combining elegant experimental designs with a sound theoretical framework. Excellent work!

Synaptic plasticity differs for D1- and D2-striatal neurons

2008-09-01T10:48:00.002-04:00

Neurons in the same region are not created equal. The exact cell type matters, and matters a lot.

Dichotomous Dopaminergic Control of Striatal Synaptic Plasticity
Weixing Shen,1 Marc Flajolet,2 Paul Greengard,2 D. James Surmeier1*

At synapses between cortical pyramidal neurons and principal striatal medium spiny neurons (MSNs), postsynaptic D1 and D2 dopamine (DA) receptors are postulated to be necessary for the induction of long-term potentiation and depression, respectively—forms of plasticity thought to underlie associative learning. Because these receptors are restricted to two distinct MSN populations, this postulate demands that synaptic plasticity be unidirectional in each cell type. Using brain slices from DA receptor transgenic mice, we show that this is not the case. Rather, DA plays complementary roles in these two types of MSN to ensure that synaptic plasticity is bidirectional and Hebbian. In models of Parkinson's disease, this system is thrown out of balance, leading to unidirectional changes in plasticity that could underlie network pathology and symptoms."

What are the functional consequences of the differential regulatory rules for synaptic plasticity in D1- and D2-striatal neurons?

In the absence of behaviorally important stimuli, DA neuronsspike autonomously to maintain striatal DA concentrations atlevels sufficient to keep high-affinity D2 DA receptors active,but not low affinity D1 DA receptors —in principleenabling bidirectional, Hebbian plasticity in D2 MSNs, but notin D1 MSNs, where the low level of D1 receptor activity shouldpermit only LTD. However, when behaviorally important stimulidrive phasic spiking of mesencephalic DA neurons, striatal DAlevels rise transiently and activate D1 DA receptors ; thisshould enable the induction of Hebbian LTP in D1 MSNs.

Rubber hand illusion

2008-07-18T23:36:00.003-04:00

THE INVISIBLE HAND

"In the 'rubber hand' illusion, a person's hand and an adjacent rubber hand are both brushed gently. The real hand is kept out of sight. Before long, the subject's brain creates a new spatial link, imagining that the sensation in the real hand is arising where the rubber hand is."

Graduate student Matthew Mulvey of Leeds Metropolitan University has now shown that the effect will work if the researchers deliver transcutaneous electrical nerve stimulation (TENS) not to the hidden hand but to the wrist. After being primed with the illusion, subjects perceive the impulses--which hijack the nerve pathways between hand and brain--as a tingling located in the rubber hand.

This is such a cool idea to take advantage of this "rubber hand" illusion as a way of establishing sensory feedback on a neuro-prosthetic device.

Neuronal Ensemble Bursting in the Basal Forebrain Encodes Salience Irrespective of Valence

2008-07-09T13:54:00.004-04:00

Neuronal Ensemble Bursting in the Basal Forebrain Encodes Salience Irrespective of Valence
Shih-Chieh Lin and Miguel A.L. Nicolelis

Our paper finally comes out in Neuron today, accompanied by a preview from Lau and Salzman:

Although noncholinergic neurons in the basal forebrain are known to contribute to cognition, their response properties in behaving animals is unclear. In this issue of Neuron, Lin and Nicolelis demonstrate that these neurons represent the motivational salience of sensory stimuli and may modulate cortical processing to direct top-down attention.

This is our abstract and the main figure

Both reward- and punishment-related stimuli are motivationally salient and attract the attention of animals. However, it remains unclear how motivational salience is processed in the brain. Here, we show that both reward- and punishment-predicting stimuli elicited robust bursting of many noncholinergic basal forebrain (BF) neurons in behaving rats. The same BF neurons also responded with similar bursting to primary reinforcement of both valences. Reinforcement responses were modulated by expectation, with surprising reinforcement eliciting stronger BF bursting. We further demonstrate that BF burst firing predicted successful detection of near-threshold stimuli. Together, our results point to the existence of a salience-encoding system independent of stimulus valence. We propose that the encoding of motivational salience by ensemble bursting of noncholinergic BF neurons may improve behavioral performance by affecting the activity of widespread cortical circuits and therefore represents a novel candidate mechanism for top-down attention.

Reward-encoding in dorsal raphe (serotonergic) neurons

2008-05-17T09:18:00.003-04:00

Reward-dependent modulation of neuronal activity in the primate dorsal raphe nucleus.
J Neurosci. 2008 May 14;28(20):5331-43
Authors: Nakamura K, Matsumoto M, Hikosaka O

Serotonergic neurons in the dorsal raphe (DR) constitute one of the major neuromodulatory systems. What is the role of DR neurons in encoding reward, in comparison with midbrain dopaminergic neurons? The authors noted several differences: (1) DA neurons encode reward prediction error -- responding to reward only when the reward size is larger or smaller than expected. DR neurons respond to both reward and reward-predicting cues, whether or not they are expected. (2) DA neurons respond to reward with a phasic bursting response, while DR neurons show slower tonic responses.
In addition, DR neurons are heterogeneous, some prefer large while others prefer small reward. Without an independent means of verifying the neurochemical identity, it remains unclear which subset of DR neurons are serotonergic neurons.

Gamma response in EEG caused by micro-saccade

2008-05-09T20:44:00.003-04:00

Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades
Shlomit Yuval-Greenberg, Orr Tomer, Alon S. Keren, Israel Nelken and Leon Y. Deouell

Turns out that a large component of gamma oscillation responses in the EEG literature is generated by small eye movements. Caution: this is not to discount the physiological importance of gamma oscillation and its roles in cognition, as had been demonstrated in single unit studies, local field potentials, MEGs, and some EEG studies.

Heterogeneity of midbrain dopamine neurons

2008-04-27T10:57:00.001-04:00

Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system
Lammel S, Hetzel A, Hackel O, Jones I, Liss B, Roeper J

Here, we report the identification of a type of dopaminergic neuron within the mesocorticolimbic dopamine system with unconventional fast-firing properties and small DAT/TH mRNA expression ratios that selectively projects to prefrontal cortex and nucleus accumbens core and medial shell as well as to basolateral amygdala. In contrast, well-described conventional slow-firing dopamine midbrain neurons only project to the lateral shell of the nucleus accumbens and the dorsolateral striatum. Among this dual dopamine midbrain system defined in this study by converging anatomical, electrophysiological, and molecular properties, mesoprefrontal dopaminergic neurons are unique, as only they do not possess functional somatodendritic Girk2-coupled dopamine D2 autoreceptors.

I am curious to know whether mesoprefrontal DA neurons are not inhibited by apomorphine, which has been the criteria for identifying DA neurons recorded in vivo.

Neuronal oscillations and Attentional selection

2008-04-27T08:52:00.002-04:00

Entrainment of neuronal oscillations as a mechanism of attentional selection
Lakatos P, Karmos G, Mehta AD, Ulbert I, and Schroeder CE

The authors measured V1 CSD in monkeys performing an inter-modal attention task. In this task, visual and auditory stimuli were presented alternately. The task was to detect the odd ball stimulus in either the visual domain or in the auditory domain (in separate blocks). The main finding is panel A, where they showed that in layer II/III an opposite pattern of CSD profile in attend-visual condition vs. attend-auditory condition prior to stimulus onset. This is in contrast to the similar CSD patterns in layer IV-VI in both conditions. This difference reflected a layer-specific task/attention effect.
This attentional effect was interpreted as the brain exploiting the rhythmicity of stimulus presentation and recruiting a cortical oscillation at the same temporal frequency as a way to enhance stimulus processing. Alternative, the authors acknowledged that these results "may represent spontaneously occurring neural oscillations aligned by phase resetting to the structure of attended stimulus streams". In other words, it is unclear whether the stimulus resets the ongoing oscillation, or a top-down attention mechanism generates an oscillation.

Decoupling through Synchrony

2008-04-27T08:29:00.001-04:00

Decoupling through Synchrony in Neuronal Circuits with Propagation Delays
Lubenov EV & Siapas AG

This paper builds upon a powerful, yet simple, insight: that is, when two connected neurons fire at the same time (as in population bursting), the conduction delay would decrease their synaptic weight under the Hebbian STDP rule (A & B). Thus, population synchrony leads to a paradoxical weakening of synaptic connections. The consequence of this observation is that it "promotes the self-organization of spontaneously active neuronal network to a state at the border between randomness and synchrony". Applied to the synchronous activity of hippocampal network during slow-wave sleep, the authors suggest that this might provide a mechanism of selective erasure of memory traces in the hippocampus as memories are transferred to the cortex.

Neuronal Ensemble Bursting in the Basal Forebrain Encodes Salience Irrespective of Valence

2008-02-25T15:28:00.003-05:00

This is the abstract of the talk I will present this week at COSYNE meeting.

The main goal of animal behavior is to maximize reward and avoid punishment. To achieve this goal, animals similarly attend to both types of motivationally salient events despite their opposite hedonic valence. Recent evidence suggests that the opposite hedonic valence of reward and punishment are processed by different and possibly opposing neural systems. However, it remains unknown whether motivational saliency of reward and punishment is processed by the same valence-specific neural systems, or alternatively, is encoded separately as a distinct and valid neurobiological construct.

Here we show that motivational saliency is encoded by ensemble bursting of basal forebrain (BF) neurons in behaving rats. We observed that motivationally salient sensory cues that predicted either sucrose or quinine delivery in a Go/Nogo task elicited a similar brief bursting response in many BF neurons. This bursting response occurred irrespective of the cue’s sensory modality, the associated motor response and the hedonic valence of the expected outcome. BF ensemble bursting emerged as cues acquired motivational saliency and predictive ability through associative learning and diminished after the cue-outcome associations underwent extinction. The same BF neurons also responded to both primary reward (sucrose) and punishment (quinine) with highly similar bursting patterns. These salience-encoding BF neurons represented a homogeneous subset of BF non-cholinergic neurons because they did not change their average firing rate across wake-sleep states [1] and their firing properties were consistent with in vitro characterizations of BF non-cholinergic neurons [2]. Finally, the relationship between BF ensemble bursting and behavioral performance was documented by showing that BF bursting responses predicted successful detection of near-threshold tones in a tone detection task.

Our results point to the existence of an independent salience-encoding system, mediated by ensemble bursting of BF neurons. This discovery suggests that the valence and salience of attended stimuli are encoded by two major neuromodulatory systems – the midbrain dopaminergic neurons and BF non-cholinergic neurons – using similar bursting responses. Contrary to the traditional view that BF functions are mediated mostly via cholinergic neurons, our findings provide the first evidence regarding the in vivo functions of the poorly understood BF non-cholinergic neurons in behavioral contexts. The encoding of motivational saliency by BF ensemble bursting may improve behavioral performance by transiently enhancing cortical gamma oscillation power [3] and, therefore, mediating the influences of attention on cortical processing. Together, our results support the hypothesis that BF ensemble bursting represents a novel candidate mechanism for attention.

[1] Lee, M.G., et al., Cholinergic Basal Forebrain Neurons Burst with Theta during Waking and Paradoxical Sleep. J. Neurosci., 2005. 25(17): p. 4365-4369.

[2] Alonso, A., et al., Differential oscillatory properties of cholinergic and noncholinergic nucleus basalis neurons in guinea pig brain slice. Eur J Neurosci, 1996. 8(1): p. 169-82.

[3] Lin, S.C., D. Gervasoni, and M.A. Nicolelis, Fast modulation of prefrontal cortex activity by basal forebrain noncholinergic neuronal ensembles. J Neurophysiol, 2006. 96(6): p. 3209-19.

Non-cholinergic basal forebrain projections to prefrontal cortex

2008-02-21T19:08:00.003-05:00

Basal forebrain (BF), also referred to as nucleus basalis, is one of the largest neuromodulatory systems, which plays important roles in attention, arousal and the control of cortical activity and plasticity. Traditionally, these BF functions have been attributed to BF cholinergic projections to the cerebral cortex.

However, what is little known to the general neuroscience community is that BF provides more non-cholinergic projections to the cortex, mostly GABAergic neurons and some glutamatergic neurons. In a recent article by Henny P and Jones BE, the non-ACh BF projection is further investigated.

Eur J Neurosci: Projections from basal forebrain to prefrontal cortex comprise cholinergic, GABAergic and glutamatergic inputs to pyradimal cells or interneurons

This study establishes that BF projections to the prefrontal cortex consist of distinct (non-overlapping) ACh, GABAergic and glutamatergic systems, with the GABAergic system being the most prominent system (account for 52% of BF terminals). These three systems innervate all cortical layers and most prominently the deep layers (V-VI). All three systems innervate calbindin+ interneurons. In addition, BF GABAergic projections innvervation parvalbumin+ interneurons (basket and chandelier cells, which provide inhibition to the soma, promimal dendrites or axon initial segment of pyramidal cells).

What are the functions of these non-ACh BF projections? In a previous paper, I demonstrated that BF non-ACh neurons spontaneously engage in ensemble bursting. Most importantly, I showed that BF ensemble bursting may lead to transient enhancement of prefrontal cortex activity (e.g. increase gamma oscillation). This finding is consistent with BF GABAergic projections to the PV+ interneurons, which provide the anatomical substrate to transiently enhance cortical activity.

What are the behavioral functions of BF ensemble bursting? This is the topic of my current study, which I will present this week at the COSYNE meeting. More on that later.

Neuroscience goes global

2008-01-21T22:18:00.000-05:00

Neuroscience is growing fast in mainland China, as reported it Nature Neuroscience Editorial.

The Institute of Neuroscience in Shanghai, founded in 1999 and led by Berkeley's Mu-ming Poo, has inspired reform of existing institutions and the establishment of newer ones. The National Institute of Biological Sciences, opened in 2004 on the outskirts of Beijing and led from afar by Xiaodong Wang of the University of Texas Southwestern, is modeled on the Howard Hughes Medical Institute. It provides generous support to promising scientists without prescribing projects or approaches...

These new neuroscience institutes in China have been able to recruit top-notch scientists by offering competitive startups rivaling those offered in the US. These institutes thus provide enviable research environments not only for the generous funding level but, most importantly, for creating the critical research mass that is essential for producing competitive science. And they are doing really well.

Similar neuroscience institutes aiming for international excellence are also found in Japan, featuring Riken Brain Science Institute and the recently established Okinawa Institute of Science and Technology; In Portugal, there is the new Champalimaud Foundation Neuroscience Programme; In Brazil, my adviser Dr. Nicolelis is building a neuroscience institute at Natal, as part of his vision to promote science and education.

As a national of Taiwan, we are way behind in this wave. It is my sincerest hope that similar effort can find support in Taiwan. These recent developments point to the globalization of neuroscience research. These examples not only prove that building a world-class neuroscience institute can be done, it is urgent that we start now.

Ed Boyden's blog

2007-12-16T23:45:00.000-05:00

I recently came across Ed Boyden's blog on Technology Review and I thought it's worth sharing. Ed Boyden is currently a faculty at MIT media lab. I first knew of his work from researching recent developments of new optical techniques that can control neuronal activity. His recent blog entry on How to think caught my attention, which I agree with for the most part but I am not able to do it all. Here's an excerpt:

1. Synthesize new ideas constantly. Never read passively. Annotate, model, think, and synthesize while you read, even when you're reading what you conceive to be introductory stuff. That way, you will always aim towards understanding things at a resolution fine enough for you to be creative.
From: Boyden, E. S. "How to Think." Ed Boyden's Blog. Technology Review. 11/13/07. (http://www.technologyreview.com/blog/boyden/21925/).

And he's only 27 now. Wow! That's impressive!

Some useful tips on...

2007-11-24T20:45:00.000-05:00

It takes so much time and effort to complete a set of experiments and to write up a paper. After finishing the manuscript of my first paper, I thought the end was near. Of course, it was far from over. Besides the manuscript itself, there are several other components involved in getting the paper published. Here are some articles that I came across that should be helpful.

Joshua Finkelstein (Senior Editor of Nature) offers some advices on how to write a cover letter to the editor
How to review a paper and some tips from an editor
How to read rejection letters and tips on how to respond to reviewers' criticism.

Some random thoughts about this blog

2007-11-21T13:25:00.001-05:00

Obviously, I have not been posting for quite a while. Life as a postdoc has been more crazy than I imagined, and I am sure it will only get worse over time.

At SfN this year, I am pleasantly surprised by a few conversations where people actually read this blog. Thank you if you are one of those people. On the other hand, not maintaining a steady flow of post is almost like not mowing the lawn. I do feel guilty about it and I will try to keep it up.

Another interesting thing I learned is that some people thought I might be the reviewer of their paper, probably because I commented on their paper on this blog in ways similar to their reviewers. I just want to clarify that this is absolutely not the case. I post good papers that I find interesting, and write comments as a way to think more deeply about the results. That's all.

Happy Thanksgiving.

How cholinergic modulation changes cortical circuits

2007-11-21T10:14:00.000-05:00

Nature: A synaptic memory trace for cortical receptive field plasticity
Froemke RC, Merzenich MM, Schreiner CE

This is an excellent paper revealing how cholinergic modulation (by stimulating nucleus basalis, or basal forebrain) transiently changes the balance between excitation and inhibition within the cortical circuit, thus mediating specific circuit plasticity in the primary auditory cortex. In pentobarbital-anesthetized rats, the authors showed that pairing tone presentations with NB stimulations leads to immediate suppression of inhibition (<20s) followed by enhancement of excitation (start around 40s), specifically for inputs tuned at the paired frequency and blocked by muscarinic antagonist atropine. This unbalanced excitation and inhibition takes about 1-2 hours to return to baseline (provided continual auditory stimulation), thus providing a window for synaptic plasticity and modification of receptive field. Thus, NB stimulation leaves a tag of reduced inhibition (at the circuit level!), specifically for neurons that are active at the time of pairing.

The paper devotes figure 3 to address whether NB pairing specifically modifies intracortical connections vs. thalamocortical projections, and showed that only intracortical synaptic strengths are modified while thalamocortical transmission unchanged. I believe the unspoken message here is the contradicting results previously obtained in slices (by Hasselmo) that show ACh suppression of intracortical transmission and enhancement of thalamocortical transmission.

What accounts for the difference? The main difference is the preparation. Froemke et al studied cortex in vivo in anesthetized rats and electrically stimulated NB. The NB neuromodulatory systems contain both ACh and non-ACh projections that might be both important for controlling cortical activity and plasticity. More importantly, ACh release following NB stimulation better mimics the spatio-temporal pattern of physiological ACh modulation, unlike bath application of ACh agonist onto the slice. Still, the detailed mechanisms leading to opposite observations in two preparations will need to be worked out.

This is a set of technically challenging experiments. The main question I hope the authors would address in the future is regarding the temporal relationship between tone presentation and NB stimulation. In the current report, NB stimulation seems to start at the same time with tone presentation (but I cannot find detailed description of this procedure, nor the number of tone presentations and number of pairings involved). This is unlikely to be true in physiological conditions, as the activation of NB neurons naturally would lag 50-100 msec after tone onset. By the time ACh modulation reaches cortex, the neuronal activation traces induced by the 50 msec tone might have dissipated. Would NB stimulation remain effective in that case? How many pairings are required to generate a lasting effect? This should be a sufficiently small number to effective in normal learning.

Addendum: Following the thought on looking at the temporal relationship between tone presentation and NB stimulation, determining the relevant time window is important because a broad window would mean any sensory stimulus present during this period will be potentiated. Since many potential cues exist in the environment, a broad temporal window for effective NB stimulation will significantly reduce the specificity of this mechanism. This issues can be tested without intracellular recording though.

OFC and outcome encoding

2007-05-09T17:47:00.000-04:00

J Neurosci: Orbitofrontal cortex mediates outcome encoding in pavlovian but not instrumental conditioning.
Ostlund SB, Balleine BW.

I've always had the impression that in the neuroscience community among people studying mammalian behavior, the rich and solid learning theory and behavioral tradition from the psychology root have not been well appreciated as they should. We all know, or think we know, about pavlovian and instrumental conditioning. But subtle variations in these 'simple' behaviors are not so simple at all. In recent years, great stride has been made about the neural systems subserving various 'constructs' in learning theories. The circuit-level understanding of behavior is rapidly evolving. The use of rodent to dissect circuitry proved to be a powerful tool, especially in the hands of great learning theorists, such as Bernard Balleine here and also Peter Holland. As a neuroscientist studying rodent neurophysiology, I often marvel at the insights their experiments provides - usually involving no more than behavior and lesion. I highly recommend their works.

This is another solid paper from Balleine. It shows that lesions of orbitofrontal cortex impairs outcome encoding, but only in the context of pavlovian but not instrumental conditioning. In other words, the outcome representation in stimulus-outcome association (pavlovian) is mediated by OFC; while the outcome representation in action-outcome association (instrumental) is mediated by prelimbic cortex (Balleine and Dickinson 1998, see discussion in the paper).

How do they reach this conclusion? Rats are trained to learn Cue1 --> Reward1, Cue2 --> Reward2 ; then learn Action 1--> Reward1, Action 2--> Reward2; Before the testing phase, give rats free access to Reward1 until satiation, so that the value of Reward1 decreases. Under this condition, the decreased value or Reward1 is reflected in rats' tendency to perform Action 2 more often than Action 1. This action-outcome association is not impaired by OFC lesion.

However, if during the testing phase, Cue1 and Cue2 are presented, Cue1 would normally potentiate Action1, through stimulus-outcome association. This process is called outcome-specific pavlovian-instrumental transfer. OFC lesion impairs this process.

In the second experiment, train rats to learn Cue1 --> Reward1, Cue2 --> Reward2; and subsequently degrade the relationship between Cue1 and Reward1. Normal rats would selectively decrease their responses toward Cue1, but OFC lesion rats would be impaired toward both cues.

Lesion studies are great, especially in showing double dissociations, in this case the dissociation between OFC and PL in outcome encoding in two different contexts. One caution regarding the lesion approach is that brain is a dynamic structure; information flows between areas and through this combination of feed-forward and feedback processes some conclusions are reached. To translate lesion works into understanding the actual dynamics of the system is no simple task, but they are a great start.

How new imaging techniques are changing neuroscience

2007-04-09T11:20:00.000-04:00

In case you haven't caught up with it, exciting new techniques are being developed in the imaging community. These techniques can, and are already changing the landscape of neuroscience.

Several recent papers come up with intriguing tricks to control neuronal activity with LIGHT at millisecond temporal resolution (a review). The idea is to introduce light-driven ion channels from algae into mammalian cells, which turn lights into either excitatory (channelrhodopsin-2, see this and this) or inhibitory currents (chloride channels, see this and this) . These channels appear to be non-toxic, non-adapting to prolonged stimulation, and respond to light stimulation with millisecond temporal resolution. Couple these new tools with techniques that target gene expression in selected brain region or even cell type (using virus vectors or lox/cre system), then you can turn on or off your favorite neuron by shining a light.

What can you do with this? Study the causality! The formula goes like: Does the activity of neuron type X at certain time point T is required for behavior Y. Turn it on or off at your choice. Electrical stimulation is of course no comparison. The only limitation seems to be genetic manipulations. But just like tons of knock-out mice now available, it is only a matter of time before this becomes the standard practice. Physiologists be aware!

Sleep consolidates emotional memory

2007-03-15T12:31:00.000-04:00

See Commentary in Current Biology

What does sleep has to do with memory? Many recent studies have established that sleep consolidates some types of memory, most notably procedural memory such as learning to ski or bike. However, it remains an settled issue whether sleep enhances episodic memory. This commentary nice summarized findings of two recent papers, both converging to the conclusion that sleep selectively enhances episodic memory that is emotionally salient, but not emotionally neutral words/pictures. This commentary also provides some nice references on the role of sleep in memory consolidation.

The speculations in this commentary is quite intriguing. If sleep enhances emotional memory, is it a natural protection mechanism to be insomniac when we are anxious or depressed, thus avoiding the impact of negative emotions? Does antidepressant drugs achieve their roles partly by inhibiting REM sleep? More provocatively perhaps, what about "sexual activity has a known soporific effect" and may facilitate bonding of romantic partners?

How do you keep up with the literature?

2007-03-01T21:42:00.000-05:00

Information is exploding. Just reading the table of contents from the major journals plus some interesting abstracts would take some time, not to mention the discipline needed for such habit. And yet, I was always worried that I missed out on some interesting/relevant paper. What's the solution?

Well, I have tried subscribing to journal TOCs and getting my pubmed search results sent to me weekly (set up an My NCBI account in pubmed, it's really useful). But pretty soon these email alerts were swamped in my gmail inbox, and I have many other emails to deal with. So either I ended up forget to go back to those email alerts, or that going through individual emails and find out the right piece of paper was just too time-consuming.

I wondered, if there's a simple interface where all the relevant information is at my fingertip, and I can just browse through all the relevant info (title/abstract/author) whenever I feel like it, without fearing that I lost track of something. Here's solution I started using lately, which I found quite satisfactory: using Google Reader to track pubmed RSS feed. With this trick, you can keep up with your pubmed search results through RSS feed. So whenever ANY new paper matching your search criteria comes online, it would appear in your Google Reader. You can also subscribe RSS feed of major journal TOCs from journal websites, from this website, or through pubmed. So here you go, a central deposit of all the latest journals and papers of interest only one click away (and no ads).

How do you deal with the vast amount of literature? Share your thoughts.

Random sample

2007-02-09T17:01:00.000-05:00

PNAS: Corticothalamic feedback enhances stimulus response precision in the visual system
Ian M. Andolina, Helen E. Jones, Wei Wang, and Adam M. Sillito
Why is there massive and more prominent corticothalamic feedback projection from V1 to LGN? By reversibly inactivating V1 with muscimol while simultaneously recording from LGN neurons, this paper demonstrates that LGN response timing and precision are significantly improved when cortical feedback is intact.

PNAS: Sequential structure of neocortical spontaneous activity in vivo
Artur Luczak, Peter Barthó, Stephan L. Marguet, >György Buzsáki, and Kenneth D. Harris
How does local cortical circuit organize its activity pattern? By studying the activity of many cortical neurons during DOWN-UP state transitions in anesthetized and awake rats, the authors observed stereotypical activation sequence of local cortical neurons during this transition, the pattern is especially precise during the first 100 msec. This sequence also seems to give rise to repeating motifs of population spike patterns (like cortical songs from Yuste lab). It would be really interesting to know if learning changes this activation sequence (and hence local circuitry)!

PNAS: Patients with hippocampal amnesia cannot imagine new experiences
Demis Hassabis, Dharshan Kumaran, Seralynne D. Vann, and Eleanor A. Maguire
This is quite interesting! Hippocampus is involved not only in recalling past episodic memory but also in constructing imagination of new experiences! So how much of memory is a construction process? And what does hippocampus really do? Also read comments in Science.