Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

ABSTRACT: Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajector

SUBMITTER: Goudar V 

PROVIDER: S-EPMC5851701 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature