141 citations · 179 across the 2 of their papers we have counts for
2 papers
cs.NE2017★ 141 cited
full-FORCE: A Target-Based Method for Training Recurrent Networks
Brian DePasquale, Christopher J. Cueva, Kanaka Rajan +2
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent n…
q-bio.NC2016★ 38 cited
Using Firing-Rate Dynamics to Train Recurrent Networks of Spiking Model Neurons
Brian DePasquale, Mark M. Churchland, L. F. Abbott
Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or "rate" model networks h…