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• W3204448303 abstract "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 (H input ) and learning through local connectivity changes (H local ). Learning under H input 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, H local led to only slightly larger changes in neural activity and covariance compared to H input . This similarity is due to H local 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 H input and H local , which could be exploited when designing future experiments." @default.
• W3204448303 created "2021-10-11" @default.
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• W3204448303 date "2021-10-01" @default.
• W3204448303 modified "2023-09-27" @default.
• W3204448303 title "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning" @default.
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• W3204448303 doi "https://doi.org/10.1101/2021.10.01.462728" @default.
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