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Invited Talk
in
Workshop: Symmetry and Geometry in Neural Representations

Pre-structured low-dimensional manifolds for rapid and efficient learning, memory, and inference in the brain

Ila Fiete


Abstract:

The brain constructs and combines modular structures for flexible computation. I will describe recent progress in characterizing the rigid and low-dimensional nature of some of these representations, using theoretical approaches including fully unsupervised topological characterization of neural population codes. I will then discuss models of how these rigid and modular circuits can emerge, and how they can generate, with high capacity and high data-efficiency without rewiring recurrent circuitry, cognitive maps across different variables (e.g. spatial and non-spatial) as well as across varied input dimensions.

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