The rockefeller university

Event Details

Type

Center for Studies in Physics and Biology Seminars

Speaker(s)

Carina Curto, Ph.D., Pablo J. Salame Goldman Sachs Professor of Computational Neuroscience, Brown University

Speaker bio(s)

Recurrent neural networks are capable of producing a wide variety of nonlinear dynamics, including fixed point attractors, limit cycles, quasiperiodic attractors, and chaos. How does the network architecture enable and constrain these dynamics? This question is key to understanding the role of connectomes in neural computation: while a connectome cannot fully determine the function of a network, it creates and constrains the space of possibilities. We explore these constraints in the context of threshold-linear networks, a family of toy models that are simple enough to be studied mathematically while exhibiting the full range of nonlinear dynamics. We study the bifurcation theory as a function of both synaptic weights and neuromodulation and find that different architectures provide different types of constraints. Mathematically, this can be understood via the combinatorial geometry of certain hyperplane arrangements associated to the model.

Open to

Public

Phone

(212) 327-8636

Sponsor

Melanie Lee
(212) 327-8636
leem@rockefeller.edu