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.
| Original language | English |
|---|---|
| Pages (from-to) | 141-148 |
| Number of pages | 8 |
| Journal | Journal of the Neurological Sciences |
| Volume | 369 |
| DOIs | |
| Publication status | Published - Oct 15 2016 |
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Keywords
- Computational models
- Motor learning
- Neuroplasticity
- Neurorehabilitation
- Stroke
ASJC Scopus subject areas
- Neurology
- Clinical Neurology
Cite this
Computational models and motor learning paradigms : Could they provide insights for neuroplasticity after stroke? An overview. / Kiper, Pawel; Szczudlik, Andrzej; Venneri, Annalena; Stozek, Joanna; Luque-Moreno, Carlos; Opara, Jozef; Baba, Alfonc; Agostini, Michela; Turolla, Andrea.
In: Journal of the Neurological Sciences, Vol. 369, 15.10.2016, p. 141-148.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Computational models and motor learning paradigms
T2 - Could they provide insights for neuroplasticity after stroke? An overview
AU - Kiper, Pawel
AU - Szczudlik, Andrzej
AU - Venneri, Annalena
AU - Stozek, Joanna
AU - Luque-Moreno, Carlos
AU - Opara, Jozef
AU - Baba, Alfonc
AU - Agostini, Michela
AU - Turolla, Andrea
PY - 2016/10/15
Y1 - 2016/10/15
N2 - 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.
AB - 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.
KW - Computational models
KW - Motor learning
KW - Neuroplasticity
KW - Neurorehabilitation
KW - Stroke
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UR - http://www.scopus.com/inward/citedby.url?scp=84982078829&partnerID=8YFLogxK
U2 - 10.1016/j.jns.2016.08.019
DO - 10.1016/j.jns.2016.08.019
M3 - Review article
VL - 369
SP - 141
EP - 148
JO - Journal of the Neurological Sciences
JF - Journal of the Neurological Sciences
SN - 0022-510X
ER -