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Poster
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Gabriel Loaiza-Ganem · Sean Perkins · Karen Schroeder · Mark Churchland · John Cunningham

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #138
in Deep Learning -- Generative Models »

Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep neural network whose output are the parameters of a spline. Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models. We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to neural population data to obtain a low-dimensional representation of spiking activity. Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input. We use a newly collected dataset where a primate completes a pedaling task, and observe better dimensionality reduction with our model than with competing alternatives.

Keywords: Generative Models · Deep Learning · Neuroscience and Cognitive Science; Neuroscience and Cognitive Science -> Neural Coding; Neuroscience and Cognitive Science
[ Paper]  [ Poster] 

Author Information

Gabriel Loaiza-Ganem (Layer 6 AI)
Sean Perkins (Columbia University)
Karen Schroeder (Columbia University)
Mark Churchland (Columbia University)
John Cunningham (University of Columbia)

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