title: Tuning electrical stimulation for thalamic visual prosthesis: An autoencoder-based approach.
abstract: Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes information and imitate that code. This paper introduces an autoencoder-based approach for tuning thalamic visual prostheses. The objective of the proposed approach is to estimate electrical stimuli that are equivalent to a given natural visual stimulus, in a way such that they both elicit responses that are as similar as possible when introduced to a Lateral Geniculate Nucleus (LGN) population. Applying the proposed method to a probabilistic model of LGN neurons, results demonstrate a significant similarity between both responses with a mean correlation of 0.672 for optimal electrodes placement and 0.354 for random electrodes placement. The results indicate the efficacy of the proposed approach in estimating an electrical stimulus equivalent to a specific visual stimulus.
authors.forename: Amr
authors.initials: A
authors.lastname: Jawwad
authors.forename: Hossam H
authors.initials: HH
authors.lastname: Abolfotuh
authors.forename: Bassem
authors.initials: B
authors.lastname: Abdullah
authors.forename: Hani M K
authors.initials: HM
authors.lastname: Mahdi
authors.forename: Seif
authors.initials: S
authors.lastname: Eldawlatly
mesh_terms: D005829:Geniculate Bodies / Q000166:cytology / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D010775:Photic Stimulation; D013788:Thalamus / Q000502:physiology*; D057488:Visual Prosthesis