231 citations · 454 across the 4 of their papers we have counts for
4 papers
Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo
Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store informat…
Making brain-machine interfaces robust to future neural variability
David Sussillo, Sergey D. Stavisky, Jonathan C. Kao +2
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when…
LFADS - Latent Factor Analysis via Dynamical Systems
David Sussillo, Rafal Jozefowicz, L. F. Abbott +1
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data sho…
Random Walk Initialization for Training Very Deep Feedforward Networks
David Sussillo, L. F. Abbott
Training very deep networks is an important open problem in machine learning. One of many difficulties is that the norm of the back-propagated error gradient can grow or decay expo…