Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A large-scale neural network training framework for generalized estimation of single-trial population dynamics

Mohammad Reza Keshtkaran, View ORCID ProfileAndrew R. Sedler, View ORCID ProfileRaeed H. Chowdhury, Raghav Tandon, Diya Basrai, Sarah L. Nguyen, View ORCID ProfileHansem Sohn, Mehrdad Jazayeri, Lee E. Miller, View ORCID ProfileChethan Pandarinath
doi: https://doi.org/10.1101/2021.01.13.426570
Mohammad Reza Keshtkaran
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew R. Sedler
2Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Andrew R. Sedler
Raeed H. Chowdhury
3Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
4Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Raeed H. Chowdhury
Raghav Tandon
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Diya Basrai
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
5Physiology and Neuroscience, University of California San Diego, La Jolla, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sarah L. Nguyen
6College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hansem Sohn
7Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Hansem Sohn
Mehrdad Jazayeri
7Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lee E. Miller
8Department of Physiology, Northwestern University, Chicago, IL, USA
4Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
9Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
10Shirley Ryan AbilityLab, Chicago, IL, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chethan Pandarinath
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
11Department of Neurosurgery, Emory University, Atlanta, GA, USA
2Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Chethan Pandarinath
  • For correspondence: cpandar@emory.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics. However, the sheer volume of data and its dynamical complexity are critical barriers to uncovering and interpreting these dynamics. Deep learning methods are a promising approach due to their ability to uncover meaningful relationships from large, complex, and noisy datasets. When applied to high-D spiking data from motor cortex (M1) during stereotyped behaviors, they offer improvements in the ability to uncover dynamics and their relation to subjects’ behaviors on a millisecond timescale. However, applying such methods to less-structured behaviors, or in brain areas that are not well-modeled by autonomous dynamics, is far more challenging, because deep learning methods often require careful hand-tuning of complex model hyperparameters (HPs). Here we demonstrate AutoLFADS, a large-scale, automated model-tuning framework that can characterize dynamics in diverse brain areas without regard to behavior. AutoLFADS uses distributed computing to train dozens of models simultaneously while using evolutionary algorithms to tune HPs in a completely unsupervised way. This enables accurate inference of dynamics out-of-the-box on a variety of datasets, including data from M1 during stereotyped and free-paced reaching, somatosensory cortex during reaching with perturbations, and frontal cortex during cognitive timing tasks. We present a cloud software package and comprehensive tutorials that enable new users to apply the method without needing dedicated computing resources.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://snel-repo.github.io/autolfads/

  • https://github.com/snel-repo/autolfads

  • https://zenodo.org/record/3854034#.X_9NA-lKjlx

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted January 15, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A large-scale neural network training framework for generalized estimation of single-trial population dynamics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A large-scale neural network training framework for generalized estimation of single-trial population dynamics
Mohammad Reza Keshtkaran, Andrew R. Sedler, Raeed H. Chowdhury, Raghav Tandon, Diya Basrai, Sarah L. Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E. Miller, Chethan Pandarinath
bioRxiv 2021.01.13.426570; doi: https://doi.org/10.1101/2021.01.13.426570
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A large-scale neural network training framework for generalized estimation of single-trial population dynamics
Mohammad Reza Keshtkaran, Andrew R. Sedler, Raeed H. Chowdhury, Raghav Tandon, Diya Basrai, Sarah L. Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E. Miller, Chethan Pandarinath
bioRxiv 2021.01.13.426570; doi: https://doi.org/10.1101/2021.01.13.426570

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4399)
  • Biochemistry (9637)
  • Bioengineering (7128)
  • Bioinformatics (24959)
  • Biophysics (12679)
  • Cancer Biology (10003)
  • Cell Biology (14406)
  • Clinical Trials (138)
  • Developmental Biology (7992)
  • Ecology (12155)
  • Epidemiology (2067)
  • Evolutionary Biology (16031)
  • Genetics (10957)
  • Genomics (14785)
  • Immunology (9911)
  • Microbiology (23750)
  • Molecular Biology (9517)
  • Neuroscience (51103)
  • Paleontology (370)
  • Pathology (1547)
  • Pharmacology and Toxicology (2694)
  • Physiology (4038)
  • Plant Biology (8700)
  • Scientific Communication and Education (1512)
  • Synthetic Biology (2406)
  • Systems Biology (6461)
  • Zoology (1350)