A Statistical Description of Neural Ensemble Dynamics
The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets, providing new insights into how the brain mediates behavior. One limitation of these techniques is they do not provide information about the underlying anatomical connections among the record...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-11-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00052/full |
| _version_ | 1818049113226215424 |
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| author | John D Long Jose M Carmena |
| author_facet | John D Long Jose M Carmena |
| author_sort | John D Long |
| collection | DOAJ |
| description | The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets, providing new insights into how the brain mediates behavior. One limitation of these techniques is they do not provide information about the underlying anatomical connections among the recorded neurons within an ensemble. Moreover, the set of possible interactions grows exponentially with ensemble size. This limitation is at the heart of the challenge one confronts when interpreting these data. Several groups have attempted the challenging inverse problem of inferring the connectivity among the recorded neurons from ensemble data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track changes in the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility for describing the dynamics of ensemble data as they relate to behavior. |
| first_indexed | 2024-12-10T10:32:25Z |
| format | Article |
| id | doaj.art-77d11400164e4e1c81032c3c9c08561c |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-10T10:32:25Z |
| publishDate | 2011-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-77d11400164e4e1c81032c3c9c08561c2022-12-22T01:52:32ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882011-11-01510.3389/fncom.2011.000529847A Statistical Description of Neural Ensemble DynamicsJohn D Long0Jose M Carmena1UC BerkeleyUC BerkeleyThe growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets, providing new insights into how the brain mediates behavior. One limitation of these techniques is they do not provide information about the underlying anatomical connections among the recorded neurons within an ensemble. Moreover, the set of possible interactions grows exponentially with ensemble size. This limitation is at the heart of the challenge one confronts when interpreting these data. Several groups have attempted the challenging inverse problem of inferring the connectivity among the recorded neurons from ensemble data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track changes in the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility for describing the dynamics of ensemble data as they relate to behavior.http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00052/fulllocal field potentialdata analysisspikesKL divergenceneural ensemble data |
| spellingShingle | John D Long Jose M Carmena A Statistical Description of Neural Ensemble Dynamics Frontiers in Computational Neuroscience local field potential data analysis spikes KL divergence neural ensemble data |
| title | A Statistical Description of Neural Ensemble Dynamics |
| title_full | A Statistical Description of Neural Ensemble Dynamics |
| title_fullStr | A Statistical Description of Neural Ensemble Dynamics |
| title_full_unstemmed | A Statistical Description of Neural Ensemble Dynamics |
| title_short | A Statistical Description of Neural Ensemble Dynamics |
| title_sort | statistical description of neural ensemble dynamics |
| topic | local field potential data analysis spikes KL divergence neural ensemble data |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00052/full |
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