Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model
As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiri...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/218373 |
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| author | Duho Sin Jinsoo Kim Jee Hyun Choi Sung-Phil Kim |
| author_facet | Duho Sin Jinsoo Kim Jee Hyun Choi Sung-Phil Kim |
| author_sort | Duho Sin |
| collection | DOAJ |
| description | As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks. |
| format | Article |
| id | doaj-art-df01ee20a790422badcffffb79d2d118 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-df01ee20a790422badcffffb79d2d1182025-02-03T01:11:17ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/218373218373Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy ModelDuho Sin0Jinsoo Kim1Jee Hyun Choi2Sung-Phil Kim3Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713, Republic of KoreaDepartment of Brain and Cognitive Engineering, Korea University, Seoul 136-713, Republic of KoreaCenter for Neural Science, Korea Institute of Science and Technology, Seoul 130-722, Republic of KoreaSchool of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of KoreaAs advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks.http://dx.doi.org/10.1155/2014/218373 |
| spellingShingle | Duho Sin Jinsoo Kim Jee Hyun Choi Sung-Phil Kim Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model Journal of Applied Mathematics |
| title | Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model |
| title_full | Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model |
| title_fullStr | Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model |
| title_full_unstemmed | Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model |
| title_short | Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model |
| title_sort | neuronal ensemble decoding using a dynamical maximum entropy model |
| url | http://dx.doi.org/10.1155/2014/218373 |
| work_keys_str_mv | AT duhosin neuronalensembledecodingusingadynamicalmaximumentropymodel AT jinsookim neuronalensembledecodingusingadynamicalmaximumentropymodel AT jeehyunchoi neuronalensembledecodingusingadynamicalmaximumentropymodel AT sungphilkim neuronalensembledecodingusingadynamicalmaximumentropymodel |