5 papers
Control of neural field equations with step-function inputs
Cyprien Tamekue, ShiNung Ching
Wilson-Cowan and Amari-type models capture nonlinear neural population dynamics, providing a fundamental framework for modeling how sensory and other exogenous inputs shape activit…
Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment
Ruiqi Chen, Giacomo Vedovati, Todd Braver +1
Dynamical systems models such as recurrent neural networks (RNNs) are increasingly popular in theoretical neuroscience for hypothesis-generation and data analysis. Evaluating the d…
On the control of recurrent neural networks using constant inputs
Cyprien Tamekue, Ruiqi Chen, ShiNung Ching
This paper investigates the controllability of a broad class of recurrent neural networks widely used in theoretical neuroscience, including models of large-scale human brain dynam…
Episodically adapted network-based controllers
Sruti Mallik, ShiNung Ching
We consider the problem of distributing a control policy across a network of interconnected units. Distributing controllers in this way has a number of potential advantages, especi…
Synergistic pathways of modulation enable robust task packing within neural dynamics
Giacomo Vedovati, ShiNung Ching
Understanding how brain networks learn and manage multiple tasks simultaneously is of interest in both neuroscience and artificial intelligence. In this regard, a recent research t…