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

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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 … continued below

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Brooks, Evan May 2007.

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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.

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  • May 2007

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  • Sept. 28, 2007, 9:50 p.m.

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  • Dec. 15, 2008, 10:29 a.m.

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Thumbnail image of item number 1 in: 'Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis'.
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Thumbnail image of item number 3 in: 'Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis'.
Thumbnail image of item number 4 in: 'Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis'.

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Brooks, Evan. Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis, thesis, May 2007; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc3702/: accessed July 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .

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