BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

1 code implementation • NeurIPS 2019 • Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski

Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity.

Learning a latent manifold of odor representations from neural responses in piriform cortex

no code implementations • NeurIPS 2018 • Anqi Wu, Stan Pashkovski, Sandeep R. Datta, Jonathan W. Pillow

Our approach is based on the Gaussian process latent variable model, and seeks to map odorants to points in a low-dimensional embedding space, where distances between points in the embedding space relate to the similarity of population responses they elicit.

Composing graphical models with neural networks for structured representations and fast inference

3 code implementations • NeurIPS 2016 • Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams

We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths.

Variational Inference