104 citations · 125 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
Input Novelty as a Control Metric for Time Varying Linear Systems
Gautam Kumar, Delsin Menolascino, ShiNung Ching
This paper introduces a framework for quantitative characterization of the controllability of time-varying linear systems (or networks) in terms of input novelty. The motivation fo…
Controlling Linear Networks with Minimally Novel Inputs
Gautam Kumar, Delsin Menolascino, MohammadMehdi Kafashan +1
In this paper, we propose a novelty-based metric for quantitative characterization of the controllability of complex networks. This inherently bounded metric describes the average…
Analysis of Recurrent Linear Networks for Enabling Compressed Sensing of Time-Varying Signals
MohammadMehdi Kafashan, Anirban Nandi, ShiNung Ching
Recent interest has developed around the problem of dynamic compressed sensing, or the recovery of time-varying, sparse signals from limited observations. In this paper, we study h…
On the output of nonlinear systems excited by discrete prolate spheroidal sequences
Kyle Q. Lepage, ShiNung Ching
The discrete prolate spheroidal sequences (DPSSs) - a set of optimally bandlimited sequences with unique properties - are important to applications in both science and engineering.…