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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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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DOI: https://doi.org/10.1007/978-0-387-88630-5_10
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