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Computational Neuroscience Journal Club

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If you have a question about this talk, please contact Rodrigo Echeveste .

Laurence Aitchison will cover:

â€ĸ The hippocampus as a predictive map

â€ĸ Kimberly L Stachenfeld, Matthew M Botvinick & Samuel J Gershman

â€ĸ Nature Neuroscience (October 2017)

â€ĸ https://www.nature.com/articles/nn.4650

Abstract: A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

This talk is part of the Computational Neuroscience series.

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