Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries
- Centro Atómico Bariloche and Instituto Balseiro, Argentina
- Emory University, United States
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 introduce the unsupervised Bayesian Ising Approximation (uBIA) 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. In data recorded during singing from neurons in a vocal control region, 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 comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. 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.
Data availability
The software implementation of uBIA is available from https://github.com/dghernandez/decomotor. The data used in this work is availablefrom https://figshare.com/articles/Songbird_premotor_dictionaries/10315844.
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Bengalese finch: Spike timings and acoustic measurements for all casesPLoS Biol, doi.org/10.1371/journal.pbio.1002018.s004.
Article and author information
Author details
Damián G Hernández
Funding
National Institutes of Health (R01-EB022872)
- Damián G Hernández
- Samuel J Sober
- Ilya Nemenman
Simons Foundation (Simons Investigator in MPS)
- Ilya Nemenman
National Institutes of Health (R01-NS084844)
- Samuel J Sober
- Ilya Nemenman
National Institutes of Health (R01-NS099375)
- Damián G Hernández
- Samuel J Sober
- Ilya Nemenman
National Science Foundation (BCS-1822677 (CRCNS Program))
- Samuel J Sober
- Ilya Nemenman
KITP ((NSF) PHY-1748958)
- Ilya Nemenman
KITP ((NIH) R25GM067110)
- Ilya Nemenman
KITP ((Gordon and Betty Moore) 2919.01)
- Ilya Nemenman
Aspen Center for Physics ((NSF) PHY-1607611)
- Samuel J Sober
- Ilya Nemenman
Simons Foundation (The Simons-Emory International Consortium on Motor Control)
- Samuel J Sober
- Ilya Nemenman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Tatyana O Sharpee, Salk Institute for Biological Studies, United States
Version history
- Preprint posted: November 20, 2019 (view preprint)
- Received: March 9, 2021
- Accepted: March 19, 2022
- Accepted Manuscript published: March 22, 2022 (version 1)
- Version of Record published: April 7, 2022 (version 2)
Copyright
© 2022, Hernández et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries
eLife 11:e68192.
https://doi.org/10.7554/eLife.68192
