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Computational Neuroscience Journal Club

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If you have a question about this talk, please contact Jake Stroud.

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together.

Zoom info: https://us02web.zoom.us/j/82267089859?pwd=WXpTMzNadHZDQnZkQWtrcFVqMk9KUT09 Meeting ID: 822 6708 9859 Password: 223170

The next topic is ‘sensory-motor integration in V1’ presented by Yashar Ahmadian and Elliott Abe. Here is a brief summary of the journal club:

In the textbook view, the activity in the primary visual cortex is assumed to primarily represent the current visual stimulus. This simplification has been especially challenged by various studies of the past decade in mouse V1, indicating that various contextual and motor-behavioral variables strongly affect V1 activity and explain much of its variance. A famous example is the amplification of V1 responses during locomotion. Several findings indicate that the effect of locomotion on V1 is more complex than just a global change of gain, and should be viewed in the broader context of sensory-motor integration. An animal’s self-motion has sensory consequences that tend to be highly predictable (e.g. self-motion causes optic flow). The brain can in principle build an internal model of these “reafference signals”, and use “efference copies” of its own motor commands in order to filter out those predictable consequences so that neural responses (in at least a subset of neurons) can represent the less predictable and more relevant aspects of external sensory inputs—or so goes (one interpretation of) one specific flavor of the predictive coding hypothesis. In this JC we will review some of the evidence for such predictive coding in mouse V1 and the circuit mechanisms implementing it.

We (my student Elliott Abe and I) will focus on these papers: - https://www.biorxiv.org/content/10.1101/2020.03.25.008607v1 - https://linkinghub.elsevier.com/retrieve/pii/S0092-8674(17)30583-4 - (time allowing) https://www.sciencedirect.com/science/article/pii/S0896627317307791

This talk is part of the Computational Neuroscience series.

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