Extracting single-trial neural interaction using latent dynamical systems model
Abstract In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-02-01
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| Series: | Molecular Brain |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13041-021-00740-7 |
| _version_ | 1819170496826048512 |
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| author | Namjung Huh Sung-Phil Kim Joonyeol Lee Jeong-woo Sohn |
| author_facet | Namjung Huh Sung-Phil Kim Joonyeol Lee Jeong-woo Sohn |
| author_sort | Namjung Huh |
| collection | DOAJ |
| description | Abstract In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model. |
| first_indexed | 2024-12-22T19:36:19Z |
| format | Article |
| id | doaj.art-a33ee84de04a46eca6a8a1de8501f29d |
| institution | Directory Open Access Journal |
| issn | 1756-6606 |
| language | English |
| last_indexed | 2024-12-22T19:36:19Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Molecular Brain |
| spelling | doaj.art-a33ee84de04a46eca6a8a1de8501f29d2022-12-21T18:14:58ZengBMCMolecular Brain1756-66062021-02-0114111210.1186/s13041-021-00740-7Extracting single-trial neural interaction using latent dynamical systems modelNamjung Huh0Sung-Phil Kim1Joonyeol Lee2Jeong-woo Sohn3Department of Medical Science, College of Medicine, Catholic Kwandong UniversityDepartment of Biomedical Engineering, Ulsan National Institute of Science and TechnologyCenter for Neuroscience Imaging Research, Institute for Basic Science (IBS)Department of Medical Science, College of Medicine, Catholic Kwandong UniversityAbstract In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model.https://doi.org/10.1186/s13041-021-00740-7Neural interactionLatent dynamical systems modelCross-correlogramOptimized neural activity |
| spellingShingle | Namjung Huh Sung-Phil Kim Joonyeol Lee Jeong-woo Sohn Extracting single-trial neural interaction using latent dynamical systems model Molecular Brain Neural interaction Latent dynamical systems model Cross-correlogram Optimized neural activity |
| title | Extracting single-trial neural interaction using latent dynamical systems model |
| title_full | Extracting single-trial neural interaction using latent dynamical systems model |
| title_fullStr | Extracting single-trial neural interaction using latent dynamical systems model |
| title_full_unstemmed | Extracting single-trial neural interaction using latent dynamical systems model |
| title_short | Extracting single-trial neural interaction using latent dynamical systems model |
| title_sort | extracting single trial neural interaction using latent dynamical systems model |
| topic | Neural interaction Latent dynamical systems model Cross-correlogram Optimized neural activity |
| url | https://doi.org/10.1186/s13041-021-00740-7 |
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