Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries
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...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2022-03-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/68192 |
| _version_ | 1828108656143499264 |
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| author | Damián G Hernández Samuel J Sober Ilya Nemenman |
| author_facet | Damián G Hernández Samuel J Sober Ilya Nemenman |
| author_sort | Damián G Hernández |
| collection | DOAJ |
| description | 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, we 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. |
| first_indexed | 2024-04-11T10:50:15Z |
| format | Article |
| id | doaj.art-cca3682576fb452f8351688248814000 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T10:50:15Z |
| publishDate | 2022-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-cca3682576fb452f83516882488140002022-12-22T04:28:56ZengeLife Sciences Publications LtdeLife2050-084X2022-03-011110.7554/eLife.68192Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionariesDamián G Hernández0https://orcid.org/0000-0002-8995-7495Samuel J Sober1https://orcid.org/0000-0002-1140-7469Ilya Nemenman2https://orcid.org/0000-0003-3024-4244Department of Medical Physics, Centro Atómico Bariloche and Instituto Balseiro, Bariloche, Argentina; Department of Physics, Emory University, Atlanta, United StatesDepartment of Biology, Emory University, Atlanta, United StatesDepartment of Physics, Emory University, Atlanta, United States; Department of Biology, Emory University, Atlanta, United States; Initiative in Theory and Modeling of Living Systems, Atlanta, United StatesThe 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, we 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.https://elifesciences.org/articles/68192dictionary reconstructioncombinatorial patternspre-motor activityBengalese finch |
| spellingShingle | Damián G Hernández Samuel J Sober Ilya Nemenman Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries eLife dictionary reconstruction combinatorial patterns pre-motor activity Bengalese finch |
| title | Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries |
| title_full | Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries |
| title_fullStr | Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries |
| title_full_unstemmed | Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries |
| title_short | Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries |
| title_sort | unsupervised bayesian ising approximation for decoding neural activity and other biological dictionaries |
| topic | dictionary reconstruction combinatorial patterns pre-motor activity Bengalese finch |
| url | https://elifesciences.org/articles/68192 |
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