Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm
Open accessAbstract
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.
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These approaches supported optimization of the stimulation parameters with neural networks and a genetic algorithm, respectively.
“These approaches supported optimization of the stimulation parameters with neural networks and a genetic algorithm, respectively.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [93]
The Conclusions (page unavailable) say the paper uses an NSGA-II genetic algorithm to tune retinal-model parameters against electrophysiological recordings. That supports the genetic-algorithm portion, but the paper describes optimizing model parameters, not stimulation parameters. (LLM, conf=0.87)
Experimental — accuracy not yet measuredTwo studies were evaluated in a mouse model involving the transduction of a calcium indicator and electrophysiological measurements in RGCs to provide an objective measurement of their activity.
“Two studies were evaluated in a mouse model involving the transduction of a calcium indicator94 and electrophysiological measurements93 in RGCs to provide an objective measurement of their activity.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [93]
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References 53 · Cited by 17
Research works printed but not linked: 15
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- Authors
- Martínez-Álvarez, A., Crespo-Cano, R., Díaz-Tahoces, A., Cuenca-Asensi, S., Ferrández Vicente, J. M., & Fernández, E.
- Affiliations
- University of Alicante (ES)
- Institute of Bioengineering, University Miguel Hernández and CIBER BBN, Av. de la Universidad s/n, Elche (Alicante), 03202, Spain
- Telecomm School, Universidad Politécnica de Cartagena, Campus Muralla del Mar s/n, Cartagena (Murcia), 30202, Spain
- Universitat de Miguel Hernández d'Elx (ES)
- Venue
- International Journal of Neural Systems
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2016-08-24
- Volume
- 26
- Issue
- 07
- Pages
- 1650021
- ISSN
- 0129-0657 (ISSN-L)
- 1793-6462
- OpenAlex
- W2319413899
- Cited by
- 17
- References
- 53
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 4 · 2019: 2 · 2018: 2 · 2017: 6
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- International Journal of Neural Systems (publishedVersion)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
- RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- artificial retina, cortical visual prosthesis, multi-objective optimization, NSGA-II, genetic algorithm, visual information encoding, electrophysiological recording, parameter optimization, trade-offs
- MeSH terms
- Action Potentials, Algorithms, Animals, Chromosomes, Feasibility Studies, Mice, Inbred C57BL, Microelectrodes, Models, Neurological, Pattern Recognition, Automated, Photic Stimulation, Retina, Retinal Ganglion Cells, Vision, Ocular, Visual Cortex, Visual Perception, Tissue Culture Techniques, Neural Prostheses
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