Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data
The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2012
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| Online Access: | http://hdl.handle.net/1721.1/70044 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
| _version_ | 1826216174982004736 |
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| author | Ghosh, S. Chen, Zhe Putrino, David F. Barbieri, Riccardo Brown, Emery N. Tseng, Mitchell Sharif, Naubaha |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Ghosh, S. Chen, Zhe Putrino, David F. Barbieri, Riccardo Brown, Emery N. Tseng, Mitchell Sharif, Naubaha |
| author_sort | Ghosh, S. |
| collection | MIT |
| description | The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l2 or l1 regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods. |
| first_indexed | 2024-09-23T16:43:29Z |
| format | Article |
| id | mit-1721.1/70044 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:43:29Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
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| spelling | mit-1721.1/700442022-10-03T07:50:45Z Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data Ghosh, S. Chen, Zhe Putrino, David F. Barbieri, Riccardo Brown, Emery N. Tseng, Mitchell Sharif, Naubaha Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Chen, Zhe Putrino, David F. Barbieri, Riccardo Brown, Emery N. Tseng, Mitchell Sharif, Naubaha The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l2 or l1 regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods. National Institutes of Health (U.S.) (Grant DP1-OD003646) National Institutes of Health (U.S.) (Grant Grant R01-DA015644) National Institutes of Health (U.S.) (Grant Grant R01-HL084502 2012-04-13T21:15:35Z 2012-04-13T21:15:35Z 2011-04 Article http://purl.org/eprint/type/JournalArticle 1534-4320 1558-0210 INSPEC Accession Number: 11911332 http://hdl.handle.net/1721.1/70044 Zhe Chen et al. “Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 19.2 (2011): 121–135. Web. 13 Apr. 2012. © 2011 Institute of Electrical and Electronics Engineers 20937583 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X en_US http://dx.doi.org/10.1109/tnsre.2010.2086079 IEEE Transactions on Neural Systems and Rehabilitation Engineering Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) PubMed Central |
| spellingShingle | Ghosh, S. Chen, Zhe Putrino, David F. Barbieri, Riccardo Brown, Emery N. Tseng, Mitchell Sharif, Naubaha Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data |
| title | Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data |
| title_full | Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data |
| title_fullStr | Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data |
| title_full_unstemmed | Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data |
| title_short | Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data |
| title_sort | statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data |
| url | http://hdl.handle.net/1721.1/70044 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
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