Data Driven Models of Short-Term Synaptic Plasticity.

ABSTRACT: Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between s

SUBMITTER: Bayat Mokhtari E 

PROVIDER: S-EPMC5972196 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature