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Related Concept Videos

Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Related Experiment Video

Updated: Dec 14, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses.

Antonio Lozano1, Juan Sebastián Suárez2,3, Cristina Soto-Sánchez2,3

  • 1Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.

International Journal of Neural Systems
|July 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Neurolight, a deep-learning framework for visual neuroprosthetics. It interfaces artificial vision with the brain via microelectrodes, aiming to restore sight for the blind.

Keywords:
Visual neuroprosthesisartificial visioncomputational modelsdeep learningneural encoding

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Area of Science:

  • Neural Engineering
  • Artificial Vision
  • Computational Neuroscience

Background:

  • Visual neuroprostheses offer potential vision restoration for the blind through electrical stimulation.
  • Advancements in neural engineering, artificial vision, and AI provide a framework for developing new neurotechnologies.

Purpose of the Study:

  • To develop a Cortical Visual Neuroprosthesis for the blind (CORTIVIS).
  • To create computationally powerful tools for functional vision restoration.
  • To design a general neuroprosthesis framework with task-oriented and visual encoding modules.

Main Methods:

  • Developed computational models for retinal ganglion cell firing rates.
  • Created the Neurolight tool to interface models with intracortical microelectrodes for electrical stimulation patterns.
  • Integrated deep-learning techniques (semantic segmentation, object detection) for image pre-processing.

Main Results:

  • Implemented a complete pipeline from video stream to neurostimulation device.
  • Demonstrated the first deep-learning-based system for direct brain interface via intracortical microelectrode arrays.
  • Developed predictive models of retinal ganglion cell encoding under neurostimulation control.

Conclusions:

  • The developed framework enables the creation of electrical stimulation patterns to evoke useful perceptions.
  • This system represents a significant step towards restoring functional vision in profoundly blind individuals.
  • The integration of deep learning with neuroprosthetics opens new avenues for treating blindness.