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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc3702 |
| Date | 05 1900 |
| Creators | Brooks, Evan |
| Contributors | Monticino, Michael G., Quintanilla, John, Brand, Neal |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Copyright, Brooks, Evan, Copyright is held by the author, unless otherwise noted. All rights reserved. |
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