Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials.

ABSTRACT: We propose a new model of the read-out of spike trains that exploits the multivariate structure of responses of neural ensembles. Assuming the point of view of a read-out neuron that receives synaptic inputs from a population of projecting neurons, synaptic inputs are weighted with a heterogeneous set of weights. We propose that synaptic weights reflect the role of each neuron within the population for the computational task that the network has to solve. In our case, the computational task is discrimination of binary classes of stimuli, and weights are such as to maximize the discrimination capacity of the network. We compute synaptic weights as the feature weights of an optimal linear classifier. Once weights have been learned, they weight spike trains and allow to compute the post-synap

SUBMITTER: Koren V 

PROVIDER: S-EPMC6797168 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature