Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview

We need as many insights into neuroplasticity as possible in order to make it completely repeatable on demand. Can your doctor assure you that neuroplasticity will work using her/his stroke protocols?
http://www.sciencedirect.com/science/article/pii/S0022510X1630507X

• Pawel Kiper, PhD^{a, ,} ,
• Andrzej Szczudlik, PhD^b,
• Annalena Venneri, PhD^{a, c},
• Joanna Stozek, PhD^d,
• Carlos Luque-Moreno, PhD^{e, f},
• Jozef Opara, PhD^g,
• Alfonc Baba, MSc^a,
• Michela Agostini, MSc^a,
• Andrea Turolla, MSc^{a, c}

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doi:10.1016/j.jns.2016.08.019

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Highlights

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Computational models can be used to better understand motor control mechanisms.

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Neuroplasticity occurs in case of permanent changes of brain structure and function.

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Neuroplasticity is modulated by administration of drugs.

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Motor learning is sustained by positive interaction with external environment.

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Internal models have been described to explain the activation of voluntary movements.

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Abstract

Computational approaches for modelling the central nervous system (CNS) aim to develop theories on processes occurring in the brain that allow the transformation of all information needed for the execution of motor acts. Computational models have been proposed in several fields, to interpret not only the CNS functioning, but also its efferent behaviour. Computational model theories can provide insights into neuromuscular and brain function allowing us to reach a deeper understanding of neuroplasticity. Neuroplasticity is the process occurring in the CNS that is able to permanently change both structure and function due to interaction with the external environment. To understand such a complex process several paradigms related to motor learning and computational modeling have been put forward. These paradigms have been explained through several internal model concepts, and supported by neurophysiological and neuroimaging studies. Therefore, it has been possible to make theories about the basis of different learning paradigms according to known computational models.

Here we review the computational models and motor learning paradigms used to describe the CNS and neuromuscular functions, as well as their role in the recovery process. These theories have the potential to provide a way to rigorously explain all the potential of CNS learning, providing a basis for future clinical studies.