Inferring single-trial neural population dynamics using sequential auto-encoders
Daniel J. O’Shea,
Sergey D. Stavisky,
Jonathan C Kao,
Eric M. Trautmann,
Matthew T. Kaufman,
Stephen I. Ryu,
Leigh R. Hochberg,
Jaimie M. Henderson,
Krishna V. Shenoy,
L. F. Abbott,
Posted 20 Jun 2017
bioRxiv DOI: 10.1101/152884 (published DOI: 10.1038/s41592-018-0109-9)
Posted 20 Jun 2017
Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract ‘de-noised’ single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural population's activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.
- Downloaded 8,226 times
- Download rankings, all-time:
- Site-wide: 333 out of 89,516
- In neuroscience: 36 out of 15,926
- Year to date:
- Site-wide: 3,303 out of 89,516
- Since beginning of last month:
- Site-wide: 9,302 out of 89,516
Downloads over time
Distribution of downloads per paper, site-wide
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!