Neurons with similar tuning more likely to be connected

From the abstract: … we determine synaptic connectivity between nearby layer 2/3 pyramidal neurons in vitro, the response properties of which were first characterized in mouse visual cortex in vivo. We found that connection probability was related to the similarity of visually driven neuronal activity. Neurons with the same preference for oriented stimuli connected at twice the rate of neurons with orthogonal orientation preferences. Neurons responding similarly to naturalistic stimuli formed connections at much higher rates than those with uncorrelated responses. Bidirectional synaptic connections were found more frequently between neuronal pairs with strongly correlated visual responses….

Ho Ko, Sonja B. Hofer, Bruno Pichler, Katherine A. Buchanan, P. Jesper Sjöström, Thomas D. Mrsic-Flogel. Functional specificity of local synaptic connections in neocortical networks. Nature. 2011 May 5;473(7345):87-91. Epub 2011 Apr 10.

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Dopamine error

(pun intended). I am embarrassed to say that earlier today I remarked to a colleague that dopamine only encodes unexpected reward, not unexpected lack of reward. This is (afaik) incorrect. It has a baseline level of firing that goes down when there is an unexpected lack of reward (see fig 1 in Wolfram Schultz, Peter Dayan, P. Read Montague. A Neural Substrate of Prediction and Reward)

However, because it can only go down so far, the negative signal is clipped, which might have consequences (see Yael Niv, Michael O Duff, Peter Dayan. Dopamine, uncertainty and TD learning).

The previous article mentions that some other people think that maybe dopamine is tracking uncertainty as well as reward. This one talks about a theory that acetylcholine is related to expected uncertainty, and norepinephrine is related to unexpected uncertainty:
Angela Yu, Peter Dayan. Expected and Unexpected Uncertainty: ACh and NE in the Neocortex (huh, all those papers had Peter Dayan as one of the authors) (btw I haven’t read all of the papers I’m posting here)

Since we’re on the subject of temporal difference learning, I’ll mention that in my opinion temporal difference learning may be a model of how futures/speculators in financial markets are supposed to propagate future price changes back in time to the present (if you think of the market as a cognitive system). I haven’t formalized this idea yet, though.

Local sleep in awake rats

this experiment claims to show that

(1) when rats are sleep-deprived, small populations of rat brain neurons can fall asleep while the rest of the rat is awake, and
(2) this may correspond to performance degradation

summary:
http://arstechnica.com/science/news/2011/04/if-you-only-feel-half-awake-you-probably-are.ars

article:

http://www.nature.com/nature/journal/v472/n7344/full/nature10009.html

i haven’t read the actual article yet…

Increasing adult hippocampal neurogenesis is sufficient to improve pattern separation.

Sahay A, Scobie KN, Hill AS, O’Carroll CM, Kheirbek MA, Burghardt NS,
Fenton AA, Dranovsky A, Hen R. Increasing adult hippocampal neurogenesis is sufficient to improve
pattern separation. Nature. 2011 Apr 3

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09817.html

Abstract after the break.

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Genetic tagging of the particular neurons in the basolateral amygdala that store a particular engram

When we learn new information we use only a tiny fraction of the neurons in our brain for that particular memory trace. In order to allow the molecular study of those specific neurons we combined elements of the tet system with a promoter that is activated by high level neural activity (the cfos promoter) to generate mice in which a genetic tag can be introduced into neurons that are active at a given point in time. The tag can be maintained for a prolonged period, creating a precise record of the neural activity pattern at a specific point in time. Using fear conditioning we found that the same neurons activated during learning were reactivated when the animal recalled the fearful event. We also found that these neurons were no longer activated following memory extinction, consistent with the idea that extinction modifies a component of the original memory trace.

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Frequency of gamma oscillations routes flow of information in the hippocampus

Supplementary Figure 1:  A schematic illustrating the main finding. Slow gamma is maximal on the descending portion of the theta wave, and fast gamma peaks near the trough. Slow gamma serves to synchronize CA1 with inputs arriving from CA3, and fast gamma synchronizes CA1 with MEC input.

Supplementary Figure 1: A schematic illustrating the main finding. Slow gamma is maximal on the descending portion of the theta wave, and fast gamma peaks near the trough. Slow gamma serves to synchronize CA1 with inputs arriving from CA3, and fast gamma synchronizes CA1 with MEC input.

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