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CHARACTERIZATION OF AN ADVANCED NEURON MODEL
Title
CHARACTERIZATION OF AN ADVANCED NEURON MODEL.
Creator
Echanique, Christopher, Behal, Aman, University of Central Florida
Abstract / Description
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)...
Show moreThis 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.
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Date Issued
2012
Identifier
CFH0004259, ucf:44958
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH0004259
DATA-TRUE CHARACTERIZATION OF NEURONAL MODELS
Title
DATA-TRUE CHARACTERIZATION OF NEURONAL MODELS.
Creator
Suarez, Jose, Behal, Aman, University of Central Florida
Abstract / Description
In this thesis, a weighted least squares approach is initially presented to estimate the parameters of an adaptive quadratic neuronal model. By casting the discontinuities in the state variables at the spiking instants as an impulse train driving the system dynamics, the neuronal output is represented as a linearly parameterized model that depends on ltered versions of the input current and the output voltage at the cell membrane. A prediction errorbased weighted least squares method is...
Show moreIn this thesis, a weighted least squares approach is initially presented to estimate the parameters of an adaptive quadratic neuronal model. By casting the discontinuities in the state variables at the spiking instants as an impulse train driving the system dynamics, the neuronal output is represented as a linearly parameterized model that depends on ltered versions of the input current and the output voltage at the cell membrane. A prediction errorbased weighted least squares method is formulated for the model. This method allows for rapid estimation of model parameters under a persistently exciting input current injection. Simulation results show the feasibility of this approach to predict multiple neuronal ring patterns. Results of the method using data from a detailed ion-channel based model showed issues that served as the basis for the more robust resonate-and- re model presented. A second method is proposed to overcome some of the issues found in the adaptive quadratic model presented. The original quadratic model is replaced by a linear resonateand- re model -with stochastic threshold- that is both computational efficient and suitable for larger network simulations. The parameter estimation method presented here consists of different stages where the set of parameters is divided in to two. The rst set of parameters is assumed to represent the subthreshold dynamics of the model, and it is estimated using a nonlinear least squares algorithm, while the second set is associated with the threshold and reset parameters as its estimated using maximum likelihood formulations. The validity of the estimation method is then tested using detailed Hodgkin-Huxley model data as well as experimental voltage recordings from rat motoneurons.
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Date Issued
2011
Identifier
CFE0003917, ucf:48724
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003917