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

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...

Full description

Bibliographic Details
Main Author: Brooks, Evan
Other Authors: Monticino, Michael G.
Format: Others
Language:English
Published: University of North Texas 2007
Subjects:
Complexity
neural network
multi-agent model
sample entropy
Neural networks (Neurobiology) > Mathematical models.
Neural transmission > Mathematical models.
Online Access:https://digital.library.unt.edu/ark:/67531/metadc3702/
id ndltd-unt.edu-info-ark-67531-metadc3702
record_format oai_dc
spelling ndltd-unt.edu-info-ark-67531-metadc37022017-03-17T08:35:50Z Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis Brooks, Evan Complexity neural network multi-agent model sample entropy Neural networks (Neurobiology) -- Mathematical models. Neural transmission -- Mathematical models. 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. University of North Texas Monticino, Michael G. Quintanilla, John Brand, Neal 2007-05 Thesis or Dissertation Text oclc: 174144166 https://digital.library.unt.edu/ark:/67531/metadc3702/ ark: ark:/67531/metadc3702 English Public Copyright Brooks, Evan Copyright is held by the author, unless otherwise noted. All rights reserved.
collection NDLTD
language English
format Others
sources NDLTD
topic Complexity
neural network
multi-agent model
sample entropy
Neural networks (Neurobiology) -- Mathematical models.
Neural transmission -- Mathematical models.
spellingShingle Complexity
neural network
multi-agent model
sample entropy
Neural networks (Neurobiology) -- Mathematical models.
Neural transmission -- Mathematical models.
Brooks, Evan
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.
author2 Monticino, Michael G.
author_facet Monticino, Michael G.
Brooks, Evan
author Brooks, Evan
author_sort Brooks, Evan
title Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis
title_short Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis
title_full Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis
title_fullStr Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis
title_full_unstemmed Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis
title_sort determining properties of synaptic structure in a neural network through spike train analysis
publisher University of North Texas
publishDate 2007
url https://digital.library.unt.edu/ark:/67531/metadc3702/
work_keys_str_mv AT brooksevan determiningpropertiesofsynapticstructureinaneuralnetworkthroughspiketrainanalysis
_version_ 1718429677390921728

Similar Items