Birs- 22w5047: Dynamical Principles of Biological and Artificial Neural Networks

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    (Carnegie Mellon) : Reverse engineering representations in real brains using artificial neural networks

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    (MILA) : Transients and non-stationarity: Not just 'not yet' and 'not for long'

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    (McGill) : The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

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    (EPFL) : Dynamics of memory retrieval in hippocampus

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    (Sao Paolo) : How does the brain encode statistical regularities? Rissanen meets von Helmholtz.

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    (UT Austin) : Mapping timescales of cortical language processing

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    (York) : Searching for observable signatures of predictive hierarchical learning in neocortex

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    (Penn State) : Modeling central pattern generator circuits with combinatorial threshold linear networks

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  9. Title:
    (Columbia) : Structure, Function, and Learning in Biological and Artificial Neural Networks

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