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Updated: Jul 6, 2026

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Published on: December 2, 2022

Sensitivity derivatives for flexible sensorimotor learning.

M N Abdelghani¹, T P Lillicrap, D B Tweed

¹Department of Physiology, University of Toronto, Toronto, Ontario, Canada. mohamed.abdelghani@utoronto.ca

Neural Computation

|March 14, 2008

Summary

This summary is machine-generated.

Biological adaptive controllers learn sensitivity derivatives through implicit supervision, a brain-based mechanism. This explains flexible sensorimotor learning and adaptation to complex environments.

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Area of Science:

Neuroscience
Control Theory
Motor Learning

Background:

Adaptive controllers require sensitivity derivatives to optimize performance.
The learning mechanism for these derivatives in biological sensorimotor control remains unexplained.
Some theories propose innate knowledge of derivatives, contrasting with neural system adaptability.

Purpose of the Study:

To investigate how sensitivity derivatives are learned in biological sensorimotor control.
To propose a novel learning mechanism for these crucial control variables.
To explain the flexibility and efficiency of sensorimotor adaptation.

Main Methods:

Theoretical modeling of information transport in neural systems.
Analysis of existing sensorimotor learning paradigms.
Conceptual framework development for implicit supervision.

Main Results:

Sensitivity derivative knowledge is not solely innate.
A mechanism termed 'implicit supervision' is proposed for learning these derivatives.
Implicit supervision utilizes available brain information transport pathways.

Conclusions:

Implicit supervision provides a plausible explanation for rapid and flexible sensorimotor learning.
This mechanism accounts for adaptation in high-dimensional workspaces and tool use.
The findings challenge innate-only theories and highlight learned derivative acquisition.