Characterization of a Spiking Neuron Model via a Linear Approach

<p>In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive threshold. In our two-stage estimation approach, a linear least squares method and a 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 spike locations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptive threshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equipped with an adaptive threshold, a constrained iterative linear least squares method is implemented to fit the model to the reference data. Through manipulation of the system dynamics, the threshold voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parametrized realizable model is then utilized inside a prediction error based framework to identify the threshold parameters with the purpose of predicting single neuron precise firing times. This estimation scheme is evaluated using both synthetic data obtained from an exact model as well as the experimental data obtained from in vitro rat somatosensory cortical neurons. Results show the ability of this approach to fit the MAT model to different types of reference data.</p>

Grantor dc:publisher

University of Central Florida

Year

2015

Author dc:creator

• Jabalameli, Amirhossein

Contributors dc:contributor

• Behal, Aman

• Computational neuroscience
• parameter estimation
• spiking neuron model
• predicting spike times
• mat model
• adaptive threshold
• leaky integrator and fire model
• linear parametrization
• Electrical and Computer Engineering
• Electrical and Electronics
• Engineering
• <p>Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic</p>

Language dc:language

English

Identifier

CFE0005958

OAI identifier oai:identifier

oai:stars.library.ucf.edu:etd-2457