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Computational Neuroscience pp 181–191Cite as

Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 38))

Abstract

Motor learning and motor control have been the focus of intense study by researchers from various disciplines. The neural network model approach has been very successful in providing theoretical frameworks on motor learning and motor control by modeling neural and psychophysical data from multiple levels of biological complexity. Two neural network models of voluntary single-joint movement organization under normal conditions are summarized here. The models seek to explain detailed electromyographic data of rapid single-joint arm movement and identify their neural substrates. The models are successful in predicting several characteristics of voluntary movement.

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Authors and Affiliations

  1. Centre for Memory and Brain, Boston University, Boston, MA, USA

    Vassilis Cutsuridis

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  1. Vassilis Cutsuridis
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Correspondence to Vassilis Cutsuridis .

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Editors and Affiliations

  1. Department of Industrial and Systems, Engineering, Rutgers State University of New Jersey, Frelinghuysen Rd. 96, Piscataway, 08854, New Jersey, USA

    Wanpracha Chaovalitwongse

  2. Dept. Industrial & Systems, Engineering, University of Florida, Weil Hall 303, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

  3. , Dept. of Industrial & Systems Eng., University of Florida, Weil Hall 303, Gainesville, 32611, Florida, USA

    Petros Xanthopoulos

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Cutsuridis, V. (2010). Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions. In: Chaovalitwongse, W., Pardalos, P., Xanthopoulos, P. (eds) Computational Neuroscience. Springer Optimization and Its Applications(), vol 38. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88630-5_10

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