Paper Info
Title
Automatic Tuning Of A Retina Model For A Cortical Visual Neuroprosthesis Using A Multi-Objective Optimization Genetic Algorithm
Abstract
The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses.
Year
DOI
Venue
2016
10.1142/S0129065716500210
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Retinal modeling, visual neuroprostheses, multi-objective optimization, NSGA-II, evolutionary search
Computational neuroscience,Computer science,Coding (social sciences),Multi-objective optimization,Artificial intelligence,Visual perception,Genetic algorithm,Computer vision,Neuroprosthetics,Pattern recognition,Visual cortex,Machine learning,Encoding (memory)
Journal
Volume
Issue
ISSN
26
7
0129-0657
Citations 
PageRank 
References 
7
0.52
24
Authors
6
Name
Order
Citations
PageRank
Antonio Martínez-Álvarez111816.22
Rubén Crespo-Cano270.52
Ariadna Díaz-Tahoces3100.95
Sergio Cuenca-Asensi46413.97
José Manuel Ferrández De Vicente54211.19
Eduardo B. Fernandez61653429.84