Applying the multivariate time-rescaling theorem to neural population models
Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any st...
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| Format: | Article |
| Language: | en_US |
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MIT Press
2011
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| Online Access: | http://hdl.handle.net/1721.1/66999 |
| _version_ | 1811076901344641024 |
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| author | Gerhard, Felipe Haslinger, Robert Heinz Pipa, Gordon |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Gerhard, Felipe Haslinger, Robert Heinz Pipa, Gordon |
| author_sort | Gerhard, Felipe |
| collection | MIT |
| description | Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based on the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models that neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem and provide a practical step-by-step procedure for applying it to testing the sufficiency of neural population models. Using several simple analytically tractable models and more complex simulated and real data sets, we demonstrate that important features of the population activity can be detected only using the multivariate extension of the test. |
| first_indexed | 2024-09-23T10:30:11Z |
| format | Article |
| id | mit-1721.1/66999 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:30:11Z |
| publishDate | 2011 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/669992022-09-27T09:50:07Z Applying the multivariate time-rescaling theorem to neural population models Gerhard, Felipe Haslinger, Robert Heinz Pipa, Gordon Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Haslinger, Robert Heinz Haslinger, Robert Heinz Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based on the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models that neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem and provide a practical step-by-step procedure for applying it to testing the sufficiency of neural population models. Using several simple analytically tractable models and more complex simulated and real data sets, we demonstrate that important features of the population activity can be detected only using the multivariate extension of the test. National Institutes of Health (U.S.) (NIH grant K25 NS052422-02) Max Planck Society for the Advancement of Science European Union (EU Grant PHOCUS, 240763) European Union (FP7-ICT-2009-C) Swiss National Science Foundation (grant number 200020-117975) Stiftung Polytechnische Gesellschaft (Frankfurt am Main, Germany) 2011-11-10T15:24:10Z 2011-11-10T15:24:10Z 2011-05 Article http://purl.org/eprint/type/JournalArticle 0899-7667 1530-888X http://hdl.handle.net/1721.1/66999 Gerhard, Felipe, Robert Haslinger, and Gordon Pipa. “Applying the Multivariate Time-Rescaling Theorem to Neural Population Models.” Neural Computation 23 (2011): 1452-1483. © 2011 Massachusetts Institute of Technology. en_US http://dx.doi.org/10.1162/NECO_a_00126 Neural Computation Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press MIT Press |
| spellingShingle | Gerhard, Felipe Haslinger, Robert Heinz Pipa, Gordon Applying the multivariate time-rescaling theorem to neural population models |
| title | Applying the multivariate time-rescaling theorem to neural population models |
| title_full | Applying the multivariate time-rescaling theorem to neural population models |
| title_fullStr | Applying the multivariate time-rescaling theorem to neural population models |
| title_full_unstemmed | Applying the multivariate time-rescaling theorem to neural population models |
| title_short | Applying the multivariate time-rescaling theorem to neural population models |
| title_sort | applying the multivariate time rescaling theorem to neural population models |
| url | http://hdl.handle.net/1721.1/66999 |
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