3 papers
cs.LG2025
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…
math.OC2024
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…
cs.LG2024
DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models
Ruiqi Chen, Giacomo Vedovati, Todd Braver +1
Dynamical system models such as Recurrent Neural Networks (RNNs) have become increasingly popular as hypothesis-generating tools in scientific research. Evaluating the dynamics in…