Neural manifold under plasticity in a goal driven learning behaviour.

ABSTRACT: Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our fi

SUBMITTER: Feulner B 

PROVIDER: S-EPMC7864452 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature