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Characterization of a Spiking Neuron Model via a Linear Approach

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Date Issued:
2015
Abstract/Description:
In the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not obtained and the predicted model is unsuccessful in duplicating the spikelocations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptivethreshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equippedwith an adaptive threshold, a constrained iterative linear least squares method is implemented tofit the model to the reference data. Through manipulation of the system dynamics, the thresholdvoltage can be obtained as a realizable model that is linear in the unknown parameters. This linearlyparametrized realizable model is then utilized inside a prediction error based framework to identifythe threshold parameters with the purpose of predicting single neuron precise firing times. Thisestimation scheme is evaluated using both synthetic data obtained from an exact model as well asthe experimental data obtained from in vitro rat somatosensory cortical neurons. Results show theability of this approach to fit the MAT model to different types of reference data.
Title: Characterization of a Spiking Neuron Model via a Linear Approach.
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Name(s): Jabalameli, Amirhossein, Author
Behal, Aman, Committee Chair
Hickman, James, Committee Member
Haralambous, Michael, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2015
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not obtained and the predicted model is unsuccessful in duplicating the spikelocations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptivethreshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equippedwith an adaptive threshold, a constrained iterative linear least squares method is implemented tofit the model to the reference data. Through manipulation of the system dynamics, the thresholdvoltage can be obtained as a realizable model that is linear in the unknown parameters. This linearlyparametrized realizable model is then utilized inside a prediction error based framework to identifythe threshold parameters with the purpose of predicting single neuron precise firing times. Thisestimation scheme is evaluated using both synthetic data obtained from an exact model as well asthe experimental data obtained from in vitro rat somatosensory cortical neurons. Results show theability of this approach to fit the MAT model to different types of reference data.
Identifier: CFE0005958 (IID), ucf:50803 (fedora)
Note(s): 2015-12-01
M.S.E.E.
Engineering and Computer Science, Electrical Engineering and Computer Engineering
Masters
This record was generated from author submitted information.
Subject(s): computational neuroscience -- parameter estimation -- spiking neuron model -- predicting spike times -- MAT model -- adaptive threshold -- leaky integrator and fire model -- linear parametrization
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005958
Restrictions on Access: campus 2016-12-15
Host Institution: UCF

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