Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data
A basic task in understanding the neural mechanism of learning andadaptation is to detect and characterize neural interactions andtheir changes in response to new experiences. Recent experimentalwork has indicated that neural interactions in the primary motorcortex of the monkey brain tend to change...
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AIMS Press
2004-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2005.2.1 |
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| author | Liqiang Zhu Ying-Cheng Lai Frank C. Hoppensteadt Jiping He |
| author_facet | Liqiang Zhu Ying-Cheng Lai Frank C. Hoppensteadt Jiping He |
| author_sort | Liqiang Zhu |
| collection | DOAJ |
| description | A basic task in understanding the neural mechanism of learning andadaptation is to detect and characterize neural interactions andtheir changes in response to new experiences. Recent experimentalwork has indicated that neural interactions in the primary motorcortex of the monkey brain tend to change their preferreddirections during adaptation to an external force field. Toquantify such changes, it is necessary to develop computationalmethodology for data analysis. Given that typical experimentaldata consist of spike trains recorded from individual neurons,probing the strength of neural interactions and their changes isextremely challenging. We recently reported in a briefcommunication [Zhu et al., Neural Computations 15 ,2359 (2003)] a general procedure to detect and quantify the causalinteractions among neurons, which is based on the method ofdirected transfer function derived from a class of multivariate,linear stochastic models. The procedure was applied to spiketrains from neurons in the primary motor cortex of the monkeybrain during adaptation, where monkeys were trained to learn a newskill by moving their arms to reach a target under externalperturbations. Our computation and analysis indicated that theadaptation tends to alter the connection topology of theunderlying neural network, yet the average interaction strength inthe network is approximately conserved before and after theadaptation. The present paper gives a detailed account of thisprocedure and its applicability to spike-train data in terms ofthe hypotheses, theory, computational methods, control test, andextensive analysis of experimental data. |
| format | Article |
| id | doaj-art-60919459e03a4979a401040e8efb0ae6 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2004-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-60919459e03a4979a401040e8efb0ae62025-01-24T01:47:55ZengAIMS PressMathematical Biosciences and Engineering1551-00182004-10-012112310.3934/mbe.2005.2.1Characterization of Neural Interaction During Learning and Adaptation from Spike-Train DataLiqiang Zhu0Ying-Cheng Lai1Frank C. Hoppensteadt2Jiping He3Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287-5706Courant Institute, New York University, New York, NY 10012Department of Bioengineering, Arizona State University, Tempe, AZ 85281A basic task in understanding the neural mechanism of learning andadaptation is to detect and characterize neural interactions andtheir changes in response to new experiences. Recent experimentalwork has indicated that neural interactions in the primary motorcortex of the monkey brain tend to change their preferreddirections during adaptation to an external force field. Toquantify such changes, it is necessary to develop computationalmethodology for data analysis. Given that typical experimentaldata consist of spike trains recorded from individual neurons,probing the strength of neural interactions and their changes isextremely challenging. We recently reported in a briefcommunication [Zhu et al., Neural Computations 15 ,2359 (2003)] a general procedure to detect and quantify the causalinteractions among neurons, which is based on the method ofdirected transfer function derived from a class of multivariate,linear stochastic models. The procedure was applied to spiketrains from neurons in the primary motor cortex of the monkeybrain during adaptation, where monkeys were trained to learn a newskill by moving their arms to reach a target under externalperturbations. Our computation and analysis indicated that theadaptation tends to alter the connection topology of theunderlying neural network, yet the average interaction strength inthe network is approximately conserved before and after theadaptation. The present paper gives a detailed account of thisprocedure and its applicability to spike-train data in terms ofthe hypotheses, theory, computational methods, control test, andextensive analysis of experimental data.https://www.aimspress.com/article/doi/10.3934/mbe.2005.2.1neural learningprimary motor cortexgranger causality.multivariate analysisneural interactiondirected transfer function |
| spellingShingle | Liqiang Zhu Ying-Cheng Lai Frank C. Hoppensteadt Jiping He Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data Mathematical Biosciences and Engineering neural learning primary motor cortex granger causality. multivariate analysis neural interaction directed transfer function |
| title | Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data |
| title_full | Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data |
| title_fullStr | Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data |
| title_full_unstemmed | Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data |
| title_short | Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data |
| title_sort | characterization of neural interaction during learning and adaptation from spike train data |
| topic | neural learning primary motor cortex granger causality. multivariate analysis neural interaction directed transfer function |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2005.2.1 |
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