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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

A machine learning framework to optimize optic nerve electrical stimulation for vision restoration.

Simone Romeni¹, Davide Zoccolan², Silvestro Micera^1,3

¹Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Patterns (New York, N.Y.)

|July 21, 2021

Summary

This summary is machine-generated.

Machine learning optimizes artificial vision by modeling the visual pathway with a convolutional neural network (CNN). This approach refines electrical stimulation protocols for the optic nerve, aiding in restoring sight for blind individuals.

Keywords:

convolutional neural networks genetic algorithms neuroprosthetics optic nerve stimulation optimization sensory restoration vision restoration

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Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Optic nerve electrical stimulation shows promise for vision restoration in blindness.
Machine learning (ML) requires system models for generating training data to optimize stimulation protocols.

Purpose of the Study:

To develop and evaluate an ML-driven framework for optimizing optic nerve stimulation protocols.
To utilize a convolutional neural network (CNN) as a model of the ventral visual stream for simulating visual processing.

Main Methods:

A CNN modeled the ventral visual stream, with a genetic algorithm optimizing optic nerve stimulation patterns.
A point-source model simulated electrode array activation for static and dynamic scenes.
Psychophysical data were used to validate the stimulation framework.

Main Results:

The ML framework successfully generated stimulation protocols compatible with natural vision.
The genetic algorithm effectively refined CNN unit activations to achieve desired patterns.
The point-source model facilitated simulation of electrode activation patterns.

Conclusions:

ML approaches, using CNNs and genetic algorithms, can optimize and personalize neuroprosthetic systems for vision restoration.
This framework offers a viable method for developing effective visual neuroprosthetics.
Simulating visual processing pathways is crucial for advancing artificial vision technologies.