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

Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds

Damián G. Hernández, Samuel J. Sober, Ilya Nemenman
doi: https://doi.org/10.1101/849034
Damián G. Hernández
1Department of Medical Physics, Centro Atómico Bariloche and Instituto Balseiro, Bariloche 8400, Argentina
2Department of Physics, Emory University, Atlanta, GA 30322, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samuel J. Sober
3Department of Biology, Emory University, Atlanta, GA 30322, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ilya Nemenman
2Department of Physics, Emory University, Atlanta, GA 30322, USA
3Department of Biology, Emory University, Atlanta, GA 30322, USA
4Initiative in Theory and Modeling of Living Systems, Atlanta, GA 30322, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ilya.nemenman@emory.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein’s function based on its sequence, we still do not understand how to accurately predict an organism’s behavior based on neural activity. Here we derive a method for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. Our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such dictionaries of important spike patterns – rather than merely identifying the timescale on which such patterns exist, as in some prior approaches – opens the door for understanding fine motor control and the neural bases of sensorimotor learning in animals. For example, for the first time, we identify differences in encoding motor exploration versus typical behavior. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.

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 November 20, 2019.
Download PDF
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.
Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds
(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
Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds
Damián G. Hernández, Samuel J. Sober, Ilya Nemenman
bioRxiv 849034; doi: https://doi.org/10.1101/849034
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds
Damián G. Hernández, Samuel J. Sober, Ilya Nemenman
bioRxiv 849034; doi: https://doi.org/10.1101/849034

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

  • Biophysics
Subject Areas
All Articles
  • Animal Behavior and Cognition (2518)
  • Biochemistry (4968)
  • Bioengineering (3473)
  • Bioinformatics (15185)
  • Biophysics (6886)
  • Cancer Biology (5380)
  • Cell Biology (7718)
  • Clinical Trials (138)
  • Developmental Biology (4521)
  • Ecology (7135)
  • Epidemiology (2059)
  • Evolutionary Biology (10211)
  • Genetics (7504)
  • Genomics (9774)
  • Immunology (4826)
  • Microbiology (13186)
  • Molecular Biology (5130)
  • Neuroscience (29368)
  • Paleontology (203)
  • Pathology (836)
  • Pharmacology and Toxicology (1461)
  • Physiology (2131)
  • Plant Biology (4738)
  • Scientific Communication and Education (1008)
  • Synthetic Biology (1337)
  • Systems Biology (4003)
  • Zoology (768)