Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

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

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity in the motor cortex. Experimental studies suggest that these changes originate from altered inputs (Hinput) rather than from changes in local connectivity (Hlocal), as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent neural network to qualitatively test this interpretation. As expected, Hinput resulted in small activity changes and largely preserved covariance. Surprisingly given the presumed dependence of stable covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in activity and covariance, still within the range of experimental recordings. This similarity is due to Hlocal only requiring small, correlated connectivity changes for successful adaptation. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

Date: 2022
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DOI: 10.1038/s41467-022-32646-w

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