Journal
NATURE METHODS
Volume 15, Issue 10, Pages 805-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41592-018-0109-9
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Funding
- US National Institutes of Health [MH093338]
- Gatsby Charitable Foundation through the Gatsby Initiative in Brain Circuitry at Columbia University
- Simons Foundation
- Swartz Foundation
- Harold and Leila Y. Mathers Foundation
- Kavli Institute for Brain Science at Columbia University
- Craig H. Neilsen Foundation
- Stanford Dean's Fellowship
- ALS Association's Milton Safenowitz Postdoctoral Fellowship
- NIH-NINDS [T-R01NS076460]
- NIH-NIMH [T-R01MH09964703]
- NIH Director's Pioneer award [8DP1HD075623]
- DARPA-DSO 'REPAIR' award [N66001-10-C-2010]
- DARPA-BTO 'NeuroFAST' award [W911NF-14-2-0013]
- Simons Foundation Collaboration on the Global Brain award [325380]
- Howard Hughes Medical Institute
- NIH-NIDCD [R01DC014034]
- Stanford BioX-NeuroVentures, Stanford Institute for Neuro-Innovation and Translational Neuroscience
- Garlick Foundation
- Reeve Foundation
- Rehabilitation Research and Development Service, Department of Veterans Affairs [B6453R]
- MGH-Deane Institute for Integrated Research on Atrial Fibrillation and Stroke
- Executive Committee on Research, Massachusetts General Hospital
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [DP1HD075623] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH093338] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS076460] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS [R01DC014034, R01DC009899] Funding Source: NIH RePORTER
- Veterans Affairs [I01RX002295] Funding Source: NIH RePORTER
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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.
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