Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries

• Type of Material: Article
• Authors: Damián G Hernandez, Centro Atómico Bariloche; Samuel Sober, Emory University; Ilya Nemenman, Emory University
• Language: English
• Date: 2022-03-22
• Publisher: eLIFE SCIENCES PUBL LTD
• Publication Version: Final Published Version
• Copyright Statement: © 2022, Hernández et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.7554/eLife.68192
• Title of Journal or Parent Work: ELIFE
• Volume: 11
• Grant/Funding Information: National Institutes of Health R01-NS099375 to Damián G Hernández, Samuel J Sober, Ilya Nemenman.; National Institutes of Health R01-NS084844 to Samuel J Sober, Ilya Nemenman.; Simons Foundation Simons Investigator in MPS to Ilya Nemenman.; Simons Foundation The Simons-Emory International Consortium on Motor Control to Samuel J Sober, Ilya Nemenman.; National Science Foundation BCS-1822677 (CRCNS Program) to Samuel J Sober, Ilya Nemenman.; This paper was supported by the following grants:; National Institutes of Health R01-EB022872 to Damián G Hernández, Samuel J Sober, Ilya Nemenman.
• Keywords: Life Sciences & Biomedicine; Science & Technology; PATTERNS; combinatorial patterns; CIRCUIT; Other; BIRDSONG; pre-motor activity; VARIABILITY; UNDERLIES; dictionary reconstruction; PRECISION; MAXIMUM-ENTROPY MODELS; INFORMATION; Bengalese finch; Life Sciences & Biomedicine - Other Topics; NETWORKS; Biology
• Research Categories: Physics, Nuclear; Biology, General

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.