Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons

Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons

Bibliographic Details
Main Author: White, William E.
Language:English
Published: Ohio University / OhioLINK 2013
Subjects:
Neurobiology
stomatogastric ganglion
conductance neuron models
lobster
evolutionary algorithms
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1368055391
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ohiou13680553912021-08-03T05:23:42Z Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons White, William E. Neurobiology stomatogastric ganglion conductance neuron models lobster evolutionary algorithms Conductance based neuron models are commonly used to simulate the activity of real neurons. A challenge in constructing these models is that it is not currently possible to measure all parameters from a single neuron, so measurements are typically made in multiple neurons of a given type. However, neurons can produce the same activity with different parameters, and models constructed with measurements across neurons can produce incorrect behavior. A possible alternative method is fitting model parameters with recordings taken from a single neuron stimulated with a perturbation which elicits information rich responses. Investigations of the parameter space of a pyloric neuron model reveal widespread regions of neurons of nearly identical activity, so perturbation is necessary to constrain parameters. Optimized voltage clamp perturbations are designed using a genetic algorithm to elicit distinctive responses from the voltage-dependent conductances. These perturbations demonstrate superior performance in separating model neurons which behave similarly in free-running conditions, causing differences in their responses to reflect differences in parameters, and facilitating gradient descent based parameter fitting methods. When used in real neurons, they cause pyloric neurons of different anatomical types to produce characteristic responses which allow automatic classification that recapitulates their anatomical types. However, parameter fitting of a pyloric model to a real neuron's responses is likely to require multiple compartments and multiple perturbations due to the anatomical complexity of these neurons. 2013-09-26 English text Ohio University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1368055391 http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1368055391 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Neurobiology
stomatogastric ganglion
conductance neuron models
lobster
evolutionary algorithms
spellingShingle Neurobiology
stomatogastric ganglion
conductance neuron models
lobster
evolutionary algorithms
White, William E.
Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons
author White, William E.
author_facet White, William E.
author_sort White, William E.
title Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons
title_short Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons
title_full Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons
title_fullStr Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons
title_full_unstemmed Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons
title_sort use of empirically optimized perturbations for separating and characterizing pyloric neurons
publisher Ohio University / OhioLINK
publishDate 2013
url http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1368055391
work_keys_str_mv AT whitewilliame useofempiricallyoptimizedperturbationsforseparatingandcharacterizingpyloricneurons
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