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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 will be online via zoom where two presenters will jointly present a topic together.

Zoom information: https://zoom.us/j/303740271?pwd=VTNNWWtQMkw4NlEybHVxajlnMG16dz09 Meeting ID: 303 740 271 Password: 046774

The next topic is ‘redundancy and information-limiting correlations in cortical coding’. This is motivated by the recent publication of experimental results from two group’s recordings from 100s-1000s of neurons in primary visual cortex during visual discrimination tasks:

“Fundamental bounds on the fidelity of sensory cortical coding” Rumyantsev et al. (Nature 2020) https://www.nature.com/articles/s41586-020-2130-2

“Scaling of information in large neural populations reveals signatures of information-limiting correlations” Kafashan et al. (bioRxiv 2020) https://www.biorxiv.org/content/10.1101/2020.01.10.902171v1

We will start by discussing the relevant theoretical background with a brief overview of some fundamental information theory followed by two theoretical papers that will help us better understand the experimental results:

“The effect of correlated variability on the accuracy of a population code” Abbott & Dayan (Neural Computation 1999) https://www.mitpressjournals.org/doi/abs/10.1162/089976699300016827

“Information limiting correlations” Moreno-Bote et al. (Nature Neuroscience 2014) https://www.nature.com/articles/nn.3807?draft=marketing

After an overview of the theory, we will compare and discuss the results in the two experimental papers as well as what this work tells us about the brain more generally. Those interested are welcome to have a look at the papers in advance, but our aim will be to provide a comprehensive overview that does not assume prior knowledge of this particular field.

This talk is part of the Computational Neuroscience series.

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