Optimal parameter estimation of the Izhikevich single neuron model using experimental inter-spike interval (ISI) data. Kumar, G.; Aggarwal, V.; Thakor, N., V.; Schieber, M., H.; and Kothare, M., V.
Optimal parameter estimation of the Izhikevich single neuron model using experimental inter-spike interval (ISI) data [pdf]Paper  abstract   bibtex   
— We propose to use the Izhikevich single neuron model to represent a motor cortex neuron for studying a control-theoretic perspective of a neuroprosthetic system. The problem of estimating model parameters is addressed when the only available data from intracortical recordings of a neuron are the Inter-Spike Intervals (ISIs). Non-linear constrained and unconstrained optimization problems are formulated to estimate model parameters as well as synaptic inputs using ISIs data. The primal-dual interior-point method is implemented to solve the constrained optimization problem. Reasonable model parameters are estimated by solving these optimization problems which may serve as a template for studying and devel-oping a model of ensemble cortical neurons for neuroprosthesis applications.
@article{
 title = {Optimal parameter estimation of the Izhikevich single neuron model using experimental inter-spike interval (ISI) data},
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 created = {2017-06-13T22:15:45.185Z},
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 abstract = {— We propose to use the Izhikevich single neuron model to represent a motor cortex neuron for studying a control-theoretic perspective of a neuroprosthetic system. The problem of estimating model parameters is addressed when the only available data from intracortical recordings of a neuron are the Inter-Spike Intervals (ISIs). Non-linear constrained and unconstrained optimization problems are formulated to estimate model parameters as well as synaptic inputs using ISIs data. The primal-dual interior-point method is implemented to solve the constrained optimization problem. Reasonable model parameters are estimated by solving these optimization problems which may serve as a template for studying and devel-oping a model of ensemble cortical neurons for neuroprosthesis applications.},
 bibtype = {article},
 author = {Kumar, Gautam and Aggarwal, Vikram and Thakor, Nitish V and Schieber, Marc H and Kothare, Mayuresh V}
}
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