Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.

ABSTRACT: Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a c

SUBMITTER: Sailamul P 

PROVIDER: S-EPMC5691111 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature