A neural implementation model of feedback-based motor learning

Barbara Feulner, Matthew G. Perich, Lee E. Miller, Claudia Clopath () and Juan A. Gallego ()
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Barbara Feulner: Imperial College London
Matthew G. Perich: Université de Montréal
Lee E. Miller: Northwestern University
Claudia Clopath: Imperial College London
Juan A. Gallego: Imperial College London

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex — known to mediate both movement correction and motor adaptation — during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.

Date: 2025
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DOI: 10.1038/s41467-024-54738-5

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