title: Spike history neural response model.
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.
authors: Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia, tkam@unimelb.edu.au.
authors: Tatiana
authors: T
authors: Kameneva
authors: Miganoosh
authors: M
authors: Abramian
authors: Daniele
authors: D
authors: Zarelli
authors: Dragan
authors: D
authors: Nĕsić
authors: Anthony N
authors: AN
authors: Burkitt
authors: Hamish
authors: H
authors: Meffin
authors: David B
authors: DB
authors: Grayden
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001701:Bionics; D004558:Electric Stimulation; D004566:Electrodes; D005123:Eye; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D011817:Rabbits; D012160:Retina / Q000166:cytology / Q000502:physiology*; D012165:Retinal Ganglion Cells / Q000502:physiology