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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 link: https://us02web.zoom.us/j/87370603771?pwd=Z2ovbmFVMm5JRktYSkdHOFZSa0tUUT09

Meeting ID: 873 7060 3771

Password: 732070

The next topic is ‘probabilistic representations’ presented by Mate Lengyel and Wayne Soo.

Mate will begin by introducing and comparing different types of neural representations of uncertainty. Wayne will then present a very recent paper that delves into such representations by analysing monkey V1 recordings: Representation of visual uncertainty through neural gain variability https://www.nature.com/articles/s41467-020-15533-0

Wayne will then discuss a new and rapidly evolving research direction that makes use of the advent of AI in the context of probabilistic representations for constructing neural network models: Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback https://www.nature.com/articles/s41467-017-00181-8

and analysing neural data: A neural basis of probabilistic computation in visual cortex https://www.nature.com/articles/s41593-019-0554-5

This talk is part of the Computational Neuroscience series.

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