## Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis

Description:
A "complex" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.

Date:
May 2007

Creator:
Brooks, Evan

Item Type:
Thesis or Dissertation

Partner:
UNT Libraries