Journal article

Spike history neural response model

Tatiana Kameneva, Miganoosh Abramian, Daniele Zarelli, Dragan Nesic, Anthony N Burkitt, Hamish Meffin, David B Grayden

JOURNAL OF COMPUTATIONAL NEUROSCIENCE | SPRINGER | Published : 2015

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 e..

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Grants

Awarded by Australian Research Council


Funding Acknowledgements

This research was supported by the Australian Research Council (DE120102210). The Bionic Ear Institute acknowledges the support it receives from the Victorian Government through its Operational Infrastructure Support Program. This research was supported by the Australian Research Council through its Special Research Initiative in Bionic Vision Science and Technology grant to Bionic Vision Australia. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.