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

Year: 2022

Type: article

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 (H input ) rather than from changes in local connectivity (H local ), 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, H input resulted in small activity changes and largely preserved covariance. Surprisingly given the presumed dependence of stable covariance on preserved circuit connectivity, H local led to only slightly larger changes in activity and covariance, still within the range of experimental recordings. This similarity is due to H local only requiring small, correlated connectivity changes for successful adaptation. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between H input and H local , which could be exploited when designing future experiments.

Source: Nature Communications

DOI: 10.1038/s41467-022-32646-w

Authors: Barbara Feulner, Matthew G. Perich, Raeed H. Chowdhury, Lee E. Miller, Juan A. Gallego, Claudia Clopath

Institutions: Imperial College London, Université de Montréal, University of Pittsburgh, Northwestern University, Shirley Ryan AbilityLab

Language: English

Topic: Motor Control and Adaptation

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,890

Funders: Simons Foundation, Wellcome Trust, Research Councils UK, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, H2020 European Research Council

OpenAlex ID: W4294237697