no code implementations • NeurIPS 2018 • Nicholas G. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W. Pillow
To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training.
no code implementations • NeurIPS 2017 • Anqi Wu, Nicholas G. Roy, Stephen Keeley, Jonathan W. Pillow
We apply the model to spike trains recorded from hippocampal place cells and show that it compares favorably to a variety of previous methods for latent structure discovery, including variational auto-encoder (VAE) based methods that parametrize the nonlinear mapping from latent space to spike rates with a deep neural network.
no code implementations • NeurIPS 2014 • William R. Vega-Brown, Marek Doniec, Nicholas G. Roy
We develop a model by choosing the maximum entropy distribution from the set of models satisfying certain smoothness and independence criteria; we show that inference on this model generalizes local kernel estimation to the context of Bayesian inference on stochastic processes.