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Title: Method and system for training a visual prosthesis

Abstract

A method for training a visual prosthesis includes presenting a non-visual reference stimulus corresponding to a reference image to a visual prosthesis patient. Training data sets are generated by presenting a series of stimulation patterns to the patient through the visual prosthesis. Each stimulation pattern in the series is determined at least in part on a received user perception input and a fitness function optimization algorithm. The presented stimulation patterns and the user perception inputs are stored and presented to a neural network off-line to determine a vision solution.

Inventors:
;
Issue Date:
Research Org.:
California Institute of Technology (CalTech), Pasadena, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1531760
Patent Number(s):
8260428
Application Number:
12/041,203
Assignee:
California Institute of Technology (Pasadena, CA)
Patent Classifications (CPCs):
A - HUMAN NECESSITIES A61 - MEDICAL OR VETERINARY SCIENCE A61F - FILTERS IMPLANTABLE INTO BLOOD VESSELS
A - HUMAN NECESSITIES A61 - MEDICAL OR VETERINARY SCIENCE A61N - ELECTROTHERAPY
DOE Contract Number:  
FG02-06ER64310
Resource Type:
Patent
Resource Relation:
Patent File Date: 2008-03-03
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 47 OTHER INSTRUMENTATION

Citation Formats

Fink, Wolfgang, and Tarbell, Mark A. Method and system for training a visual prosthesis. United States: N. p., 2012. Web.
Fink, Wolfgang, & Tarbell, Mark A. Method and system for training a visual prosthesis. United States.
Fink, Wolfgang, and Tarbell, Mark A. Tue . "Method and system for training a visual prosthesis". United States. https://www.osti.gov/servlets/purl/1531760.
@article{osti_1531760,
title = {Method and system for training a visual prosthesis},
author = {Fink, Wolfgang and Tarbell, Mark A.},
abstractNote = {A method for training a visual prosthesis includes presenting a non-visual reference stimulus corresponding to a reference image to a visual prosthesis patient. Training data sets are generated by presenting a series of stimulation patterns to the patient through the visual prosthesis. Each stimulation pattern in the series is determined at least in part on a received user perception input and a fitness function optimization algorithm. The presented stimulation patterns and the user perception inputs are stored and presented to a neural network off-line to determine a vision solution.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {9}
}

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