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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 information: https://us02web.zoom.us/j/82042228464?pwd=eFBEVjU3MFF4VmRxekFtTHhXNnIyUT09 Meeting ID: 820 4222 8464 Password: 541373

The next topic is ‘attractor networks and their relations to neural dynamics’ presented by Jake Stroud and Calvin Kao. We will provide mathematical definitions of the main classes of attractor networks along with a motivation of why they are relevant for studying the brain. We will then discuss exactly how attractor networks capture real neural activity by going through the key results of the following 5 papers:

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex, Murray et al., PNAS , 2017, https://www.pnas.org/content/114/2/394

Bump attractor dynamics in prefrontal cortex explains behavioural precision in spatial working memory, Wimmer et al., Nature Neuroscience, 2014, https://www.nature.com/articles/nn.3645

Discrete attractor dynamics underlies persistent activity in the frontal cortex, Inagaki et al., Nature, 2019, https://www.nature.com/articles/s41586-019-0919-7

Context-dependent computation by recurrent dynamics in prefrontal cortex, Mante et al., Nature, 2013, https://www.nature.com/articles/nature12742

The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep, Chaudhuri et al., Nature Neuroscience, 2019, https://www.nature.com/articles/s41593-019-0460-x

This talk is part of the Computational Neuroscience series.

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