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Verduzco-Flores SO, O'Reilly RC. How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error. Front Comput Neurosci. 2015;9:39doi: 10.3389/fncom.2015.00039.
Verduzco-Flores, S. O., & O'Reilly, R. C. (2015). How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error. Frontiers in computational neuroscience, 939. https://doi.org/10.3389/fncom.2015.00039
Verduzco-Flores, Sergio O, and O'Reilly, Randall C. "How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error." Frontiers in computational neuroscience vol. 9 (2015): 39. doi: https://doi.org/10.3389/fncom.2015.00039
Verduzco-Flores SO, O'Reilly RC. How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error. Front Comput Neurosci. 2015 Mar 24;9:39. doi: 10.3389/fncom.2015.00039. eCollection 2015. PMID: 25852535; PMCID: PMC4371707.
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Front Comput Neurosci. 2015 Mar 24;9:39. doi: 10.3389/fncom.2015.00039. eCollection 2015.

How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error.

Frontiers in computational neuroscience

Sergio O Verduzco-Flores, Randall C O'Reilly

Affiliations

  1. Computational Cognitive Neuroscience Laboratory, Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, USA.

PMID: 25852535 PMCID: PMC4371707 DOI: 10.3389/fncom.2015.00039

Abstract

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error and distal learning problems. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.

Keywords: cerebellum; complex spikes; equilibrium point; motor learning; reaching

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