English: Ila Fiete, Massachusetts Institute of Technology

Mixed Modular Codes and Remapping for Highly Generalizable Learning and Inference

It has long been hypothesized that generalizably solving complex problems involves decomposing them into simpler components and combining these parts in effective ways to solve new instances. How does the brain do so? The hippocampal complex is a rich playground for understanding how the brain constructs cognitive representations of latent variables because of its quantifiable representations of spatial information navigation. I will describe how the hippocampal complex — consisting of the hippocampus and associated areas – factorizes the world by generating separate low-dimensional manifolds to integrate and store different metric (Euclidean) variables relevant for navigation. I will show that these manifolds are rigid, persisting unchanged across tasks and states including waking and sleep. Next, I will describe how the same simple and rigid low-dimensional recurrent circuits could represent variables of different dimension and topology, with high capacity. Such generalization can be achieved by combining rigid modular representations with feedforward learning of inputs and random output projections to a sparsely active layer. These models require no rewiring of the local recurrent circuitry and thus permit relatively fast learning; the high capacity may enable lifelong learning. I will conclude by discussing how combining local metric maps with the dynamics of hippocampal remapping could permit learning of and inference on complex knowledge graphs. These models shed light on how the same neural circuits could play a role in both spatial and non-spatial cognitive mapping and inference.

Biosketch Ila Fiete is a Professor in the Department of Brain and Cognitive Sciences and the McGovern Institute at MIT. Her group seeks to understand why the brain contains particular codes, how the architecture and dynamics of neural circuits shape such codes, and how neural circuit dynamics perform desired computations that unfold over time. They are specifically interested in how the brain learns, holds memories, integrates, and performs cognitive inference and reasoning. They use analytical and computational tools, and their approach includes working closely with collaborators on specific experimental systems.

Ila Fiete obtained her Ph.D. at Harvard, under the guidance of Sebastian Seung (then at MIT). Her postdoctoral work was at the Kavli Institute for Theoretical Physics at Santa Barbara, and at Caltech, where she was a Broad Fellow. She subsequently joined the faculty of the University of Texas at Austin in the Center for Learning and Memory . Ila Fiete is an HHMI Faculty Scholar . She has been a CIFAR Senior Fellow, a McKnight Scholar , an ONR Young Investigator , an Alfred P. Sloan Foundation Fellow and a Searle Scholar.