147 citations · 719 across the 43 of their papers we have counts for
4 papers · 1 filter
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
Recurrent neural networks (RNNs) are powerful models for processing time-series data, but it remains challenging to understand how they function. Improving this understanding is of…
Generalized Shape Metrics on Neural Representations
Alex H. Williams, Erin Kunz, Simon Kornblith +1
Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interest…
Statistical Neuroscience in the Single Trial Limit
Alex H. Williams, Scott W. Linderman
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cogniti…
Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
Xinwei Yu, Matthew S. Creamer, Francesco Randi +3
We present an automated method to track and identify neurons in C. elegans, called "fast Deep Learning Correspondence" or fDLC, based on the transformer network architecture. The m…