201 citations · 1.3k across the 46 of their papers we have counts for
49 papers
KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks
James Hazelden, Laura Driscoll, Eli Shlizerman +1
Gradient Descent (GD) and its variants are the primary tool for enabling efficient training of recurrent dynamical systems such as Recurrent Neural Networks (RNNs), Neural ODEs and…
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks
Yuxiu Shao, David Dahmen, Stefano Recanatesi +2
Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that…
Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs
James Hazelden, Yuhan Helena Liu, Eli Shlizerman +1
Training networks consisting of biophysically accurate neuron models could allow for new insights into how brain circuits can organize and solve tasks. We begin by analyzing the ex…
How connectivity structure shapes rich and lazy learning in neural circuits
Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford +3
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight di…
A simple connection from loss flatness to compressed neural representations
Shirui Chen, Stefano Recanatesi, Eric Shea-Brown
Despite extensive study, the significance of sharpness -- the trace of the loss Hessian at local minima -- remains unclear. We investigate an alternative perspective: how sharpness…
Expressive probabilistic sampling in recurrent neural networks
Shirui Chen, Linxing Preston Jiang, Rajesh P. N. Rao +1
In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. H…