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

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.

Grantor dc:publisher

University of North Texas

Year dc:date

2007

Author dc:creator

• Brooks, Evan

Contributors dc:contributor

• Monticino, Michael G.
• Quintanilla, John
• Brand, Neal

• complexity
• neural network
• multi-agent model
• sample entropy
• Neural networks (Neurobiology) -- Mathematical models.
• Neural transmission -- Mathematical models.

Statement dc:rights

• Public
• Copyright
• Brooks, Evan
• Copyright is held by the author, unless otherwise noted. All rights reserved.

Language dc:language

English

Identifier

oclc: 174144166
https://digital.library.unt.edu/ark:/67531/metadc3702/
ark: ark:/67531/metadc3702

OAI identifier oai:identifier

info:ark/67531/metadc3702