RT Journal Article
SR Electronic
T1 Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.01.462728
DO 10.1101/2021.10.01.462728
A1 Feulner, Barbara
A1 Perich, Matthew G.
A1 Chowdhury, Raeed H.
A1 Miller, Lee E.
A1 Gallego, Juan Álvaro
A1 Clopath, Claudia
YR 2021
UL http://biorxiv.org/content/early/2021/10/01/2021.10.01.462728.abstract
AB Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. 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.Competing Interest StatementThe authors have declared no competing interest.