title: A cortical integrate-and-fire neural network model for blind decoding of visual prosthetic stimulation.
abstract: We present a computational model of the optic pathway which has been adapted to simulate cortical responses to visual-prosthetic stimulation. This model reproduces the statistically observed distributions of spikes for cortical recordings of sham and maximum-intensity stimuli, while simultaneously generating cellular receptive fields consistent with those observed using traditional visual neuroscience methods. By inverting this model to generate candidate phosphenes which could generate the responses observed to novel stimulation strategies, we hope to aid the development of said strategies in-vivo before being deployed in clinical settings.
authors.forename: Calvin D
authors.initials: CD
authors.lastname: Eiber
authors.forename: John W
authors.initials: JW
authors.lastname: Morley
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
authors.forename: Gregg J
authors.initials: GJ
authors.lastname: Suaning
mesh_terms: D000465:Algorithms; D000818:Animals; D001766:Blindness; D002415:Cats; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004558:Electric Stimulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010719:Phosphenes; D010775:Photic Stimulation; D011474:Prosthesis Design; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014793:Visual Cortex / Q000502:physiology*; D057488:Visual Prosthesis