Inferring single-trial neural population dynamics using sequential auto-encoders

Pandarinath, C., O'Shea, D. J., Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E. M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., Henderson, J. M., Shenoy, K. V., Abbott, L. F., Sussillo, D. (September 2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nat Methods, 15 (10). pp. 805-815. ISSN 1548-7091

URL: https://www.ncbi.nlm.nih.gov/pubmed/30224673
DOI: 10.1038/s41592-018-0109-9

Abstract

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.

Item Type: Paper
Subjects: organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons
CSHL Authors:
Communities: CSHL labs > Churchland lab
Depositing User: Matthew Dunn
Date: 17 September 2018
Date Deposited: 21 Sep 2018 18:26
Last Modified: 27 Nov 2018 17:32
Related URLs:
URI: http://repository.cshl.edu/id/eprint/37216

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