Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models
Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) mod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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Frontiers Research Foundation
2016
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| Online Access: | http://hdl.handle.net/1721.1/102351 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1826213820664643584 |
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| author | Ba, Demba Temereanca, Simona Brown, Emery N. |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Ba, Demba Temereanca, Simona Brown, Emery N. |
| author_sort | Ba, Demba |
| collection | MIT |
| description | Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble. |
| first_indexed | 2024-09-23T15:55:02Z |
| format | Article |
| id | mit-1721.1/102351 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:55:02Z |
| publishDate | 2016 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1023512022-09-29T17:03:41Z Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models Ba, Demba Temereanca, Simona Brown, Emery N. Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Ba, Demba Brown, Emery N. Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble. National Science Foundation (U.S.) (Grant 0836720) National Institutes of Health (U.S.) (Grant DA-015644) National Institutes of Health (U.S.) (Grant DP10D003646) 2016-05-02T15:46:57Z 2016-05-02T15:46:57Z 2014-02 2013-08 Article http://purl.org/eprint/type/JournalArticle 1662-5188 http://hdl.handle.net/1721.1/102351 Ba, Demba, Simona Temereanca, and Emery N. Brown. “Algorithms for the Analysis of Ensemble Neural Spiking Activity Using Simultaneous-Event Multivariate Point-Process Models.” Frontiers in Computational Neuroscience 8 (2014). https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.3389/fncom.2014.00006 Frontiers in Computational Neuroscience Creative Commons Attribution 3.0 Unported licence http://creativecommons.org/licenses/by/3.0/ application/pdf Frontiers Research Foundation Frontiers |
| spellingShingle | Ba, Demba Temereanca, Simona Brown, Emery N. Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models |
| title | Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models |
| title_full | Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models |
| title_fullStr | Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models |
| title_full_unstemmed | Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models |
| title_short | Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models |
| title_sort | algorithms for the analysis of ensemble neural spiking activity using simultaneous event multivariate point process models |
| url | http://hdl.handle.net/1721.1/102351 https://orcid.org/0000-0003-2668-7819 |
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