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Title: Spike history neural response model
Authors: Kameneva, Tatiana
Abramain, Miganoosh
Zarelli, Daniele
Nesic, Dragan
Burkitt, Anthony
Meffin, Hamish
Grayden, David
Keywords: Retinal ganglion cells
Modeling
Simulations
Closed-loop stimulation strategy
Bionic eye
Issue Date: Jun-2015
Publisher: Springer US
Citation: Kameneva, T., M. Abramian, D. Zarelli, D. Nesic, A. N. Burkitt, H. Meffin & D. B. Grayden (2015). Spike history neural response model. Journal of Computational Neuroscience 38(3): 463-481
Abstract: There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.
URI: http://repository.bionicsinstitute.org:8080/handle/123456789/169
ISSN: 0929-5313
Appears in Collections:Neurobionics Research Publications

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