Speaker: Katie Morrison
Title: Predicting neural network dynamics from connectivity: a graph-theoretic and topological approach
Abstract: Neural networks often exhibit complex patterns of activity that are shaped by the intrinsic structure of the network. For example, spontaneous sequences of neural activity have been observed in cortex and hippocampus, and patterned motor activity arises in central pattern generators for locomotion. We focus on a simplified neural network model known as Combinatorial Threshold-Linear Networks (CTLNs) in order to understand how the pattern of neural connectivity, as encoded by a directed graph, shapes the emergent nonlinear dynamics of the network. It has previously been shown that important aspects of these dynamics are controlled by the collection of stable and unstable fixed points of the network. In this talk, we highlight two different methods using covers of the connectivity graph to better understand the fixed points as well as the dynamics more broadly. These graph covers provide insight into network dynamics via either (1) the structure of the cover, e.g. its nerve, or (2) via the fixed points of the component subnetworks, which can be “glued” together to yield the fixed points of the full network. Both of these methods provide a significant dimensionality reduction of the network, giving insight into the emergent dynamics and how they are shaped by the network connectivity.
We will meet in the following coordinates (which differ from the previous zoom link):