Coding with transient trajectories in recurrent neural networks.

ABSTRACT: Following a stimulus, the neural response typically strongly varies in time and across neurons before settling to a steady-state. While classical population coding theory disregards the temporal dimension, recent works have argued that trajectories of transient activity can be particularly informative about stimulus identity and may form the basis of computations through dynamics. Yet the dynamical mechanisms needed to generate a population code based on transient trajectories have not been fully elucidated. Here we examine transient coding in a broad class of high-dimensional linear networks of recurrently connected units. We start by reviewing a well-known result that leads to a distinction between two classes of networks: networks in which all inputs lead to weak, decaying transients, a

SUBMITTER: Bondanelli G 

PROVIDER: S-EPMC7043794 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature