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Sandeep R. Datta

4 accepted papers

2022

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs

Julia C Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey Evan Markowitz +3
NeurIPS 2022accept

A core goal in systems neuroscience and neuroethology is to understand how neural circuits generate naturalistic behavior. One foundational idea is that complex naturalistic behavior may be composed of sequences of stereotyped behavioral syllables, which combine to generate rich sequences of actions…

Cited by 21Source ↗PDF ↗Scholar ↗
2019

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall +7
NeurIPS 2019poster

A fundamental goal of systems neuroscience is to understand the relationship between neural activity and behavior. Behavior has traditionally been characterized by low-dimensional, task-related variables such as movement speed or response times. More recently, there has been a growing interest in au…

Cited by 99Source ↗PDF ↗code ↗Scholar ↗
2018

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

Anqi Wu, Stan Pashkovski, Sandeep R Datta, Jonathan W Pillow
NeurIPS 2018poster

A major difficulty in studying the neural mechanisms underlying olfactory perception is the lack of obvious structure in the relationship between odorants and the neural activity patterns they elicit. Here we use odor-evoked responses in piriform cortex to identify a latent manifold specifying laten…

Cited by 42Source ↗PDF ↗Scholar ↗
2016

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

Matthew J Johnson, David K. Duvenaud, Alex Wiltschko, Ryan P. Adams, Sandeep R Datta
NeurIPS 2016poster

We propose a general modeling and inference framework that combines the complementary strengths of probabilistic graphical models and deep learning methods. Our model family composes latent graphical models with neural network observation likelihoods. For inference, we use recognition networks to pr…

Cited by 581Source ↗PDF ↗code ↗Scholar ↗