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dc.contributor.authorJi, Lin
dc.date.accessioned 2018-06-18T17:48:14Z
dc.date.available 2018-06-18T17:48:14Z
dc.date.issued 2000-05
dc.identifier.citation Ji, Lin. "The Inverse Problem of Neuron Identification." (2000) https://hdl.handle.net/1911/101948.
dc.identifier.urihttps://hdl.handle.net/1911/101948
dc.descriptionThis work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/19516
dc.description.abstract Depending on the state of neuron membrane, the inverse problem of neuron identification is divided into two categories: the passive neuron identification and the active neuron identification. In the first category, we provided a more efficient way to recover neuron parameters than the traditional approach. By exploring the impedance function meticulously, our method reveals a clean and analytical relation between the electrical properties of neurons and their response to sub-threshold current stimulation. Mathematical equations like the Hodgkin-Huxley equations and the Fitzhugh-Nagumo equations that model active neurons have been established for many years. However, the inverse problem in this category has barely started. Our research in this direction attempts to establish a proper formulation of the inverse problem and to investigate possible mathematical techniques that are needed to solve it. For the relatively simple Fitzhugh-Nagumo equations, we successfully reconstructed the nonlinear membrane conductance function and the coefficients of the recovering variable. The method is then extended to a more realistic neuron model, the Morris-Lecar model. We provide a computational strategy for systematically recovering the nonlinearity of both calcium and the potassium channels.
dc.format.extent 132 pp
dc.title The Inverse Problem of Neuron Identification
dc.type Technical report
dc.date.note May 2000
dc.identifier.digital TR00-19
dc.type.dcmi Text


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