Abstract
The central nervous system (CNS), in order to accurately control the movements we perform, needs to be constantly informed about the state of the motor apparatus. However, the quality of the control is challenged by several factors like biological noise and delays. It is widely believed that the CNS overcomes these challenges by calculating an estimation of the state, available before the sensors provide any information. This state prediction is the output of internal forward models. Control theory schemes have been used to summarize the existing knowledge about state estimation and to improve our understanding of human movement control. The first part of this chapter presents control theory frameworks that have been proposed based on the concepts of state prediction and state estimation. The concepts introduced by control theory models can only be validated, extended, or discarded based on experimental evidence. There is a plethora of studies suggesting that the cerebellum holds internal forward models, and is thus responsible for the calculation of the state prediction. The second part of this chapter reviews converging evidence from neurophysiology, neuropsychology, and behavioral neuroscience that illustrate the contribution of the cerebellum to state estimation. Functional imaging experiments, patient studies, and adaptation experiments suggest that cerebellar learning mechanisms are critical for accurate state estimation.
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Acknowledgments
RH and RCM are supported by the Wellcome Trust; MD is supported by the EU FP7 ITN “C7.” MD reviewed the theoretical models underlying state estimation and RH compiled empirical evidence. We thank Jonathan Winter for producing Fig. 57.2.
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Hardwick, R.M., Dagioglou, M., Miall, R.C. (2013). State Estimation and the Cerebellum. In: Manto, M., Schmahmann, J.D., Rossi, F., Gruol, D.L., Koibuchi, N. (eds) Handbook of the Cerebellum and Cerebellar Disorders. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1333-8_57
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DOI: https://doi.org/10.1007/978-94-007-1333-8_57
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