Abstract

This thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP) can be obtained to simplify the estimation process. With a persistently excited current input applied to the model, simulated annealing is used to efficiently determine the best model parameters that minimize the square error function between the membrane voltage reference data and data generated by the LP model. Results obtained through simulation of this approach show feasibility to predict a range of different neuron firing patterns.

Notes

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Thesis Completion

2012

Semester

Summer

Advisor

Behal, Aman

Degree

Bachelor of Science in Computer Engineering (B.S.P.E.)

College

College of Engineering and Computer Science

Degree Program

Computer Engineering

Subjects

Dissertations, Academic -- Engineering and Computer Science;Engineering and Computer Science -- Dissertations, Academic

Format

PDF

Identifier

CFH0004259

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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