Location: Gilman 132
When: March 30th at 1:30 p.m.
Title: An introduction to threshold-linear networks for neuroscience
Abstract: Threshold-linear networks (TLNs) are popular models for modeling neural activity in the brain. They are simple, recurrently-connected networks with a rich repertoire of nonlinear dynamics, including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. The resulting “graph rules” and “gluing rules” provide a direct connection between network architecture and key features of the dynamics. In this talk, I will provide an introduction to the theory of TLNs via a mix of theorems and examples. In a selection of applications, I will show how the theory enables us to design networks that perform various neural computations, such as counting stimulus pulses, position tracking, and transitioning between various locomotive gaits.
Zoom link: https://wse.zoom.us/j/95738965246