Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States
UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the br...
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| Format: | Article |
| Language: | en_US |
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MIT Press
2012
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| Online Access: | http://hdl.handle.net/1721.1/70846 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X https://orcid.org/0000-0001-7149-3584 |
| _version_ | 1811090336580108288 |
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| author | Chen, Zhe Vijayan, Sujith Barbieri, Riccardo Wilson, Matthew A. Brown, Emery N. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Chen, Zhe Vijayan, Sujith Barbieri, Riccardo Wilson, Matthew A. Brown, Emery N. |
| author_sort | Chen, Zhe |
| collection | MIT |
| description | UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process. |
| first_indexed | 2024-09-23T14:43:09Z |
| format | Article |
| id | mit-1721.1/70846 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:43:09Z |
| publishDate | 2012 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/708462022-09-29T10:12:05Z Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States Chen, Zhe Vijayan, Sujith Barbieri, Riccardo Wilson, Matthew A. Brown, Emery N. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Picower Institute for Learning and Memory Brown, Emery N. Chen, Zhe Vijayan, Sujith Barbieri, Riccardo Wilson, Matthew A. Brown, Emery N. UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process. National Institutes of Health (U.S.) (Grant R01-DA015644) National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646) National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502) National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901) 2012-05-16T16:12:27Z 2012-05-16T16:12:27Z 2009-07 Article http://purl.org/eprint/type/JournalArticle 0899-7667 1530-888X http://hdl.handle.net/1721.1/70846 Chen, Zhe et al. “Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States.” Neural Computation 21.7 (2009): 1797–1862. Web. https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X https://orcid.org/0000-0001-7149-3584 en_US http://dx.doi.org/10.1162/neco.2009.06-08-799 Neural Computation Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf MIT Press PubMed Central |
| spellingShingle | Chen, Zhe Vijayan, Sujith Barbieri, Riccardo Wilson, Matthew A. Brown, Emery N. Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States |
| title | Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States |
| title_full | Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States |
| title_fullStr | Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States |
| title_full_unstemmed | Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States |
| title_short | Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States |
| title_sort | discrete and continuous time probabilistic models and algorithms for inferring neuronal up and down states |
| url | http://hdl.handle.net/1721.1/70846 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X https://orcid.org/0000-0001-7149-3584 |
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