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The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

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Updated: May 12, 2026

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

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A hybrid bioelectronic retina-probe interface for object recognition.

Yifei Ye¹, Yunxiao Lu², Haoyang Su³

¹2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.

Biosensors & Bioelectronics

|March 27, 2025

Summary

This summary is machine-generated.

Researchers developed a flexible microelectrode array for high-quality retinal spike detection. This bioelectronic system enables image recognition and object detection, maintaining high accuracy for extended periods.

Keywords:

Image recognition Object recognition Perforated microelectrode array Retina

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Published on: August 10, 2019

Area of Science:

Bioelectronic Engineering
Neuroscience
Ophthalmology

Background:

The retina encodes visual information via spike firings, crucial for visual system research and disease therapies.
Current methods for probing retinal spikes face limitations: restricted recording channels, poor electrode-retina contact, and short device longevity.

Purpose of the Study:

To develop a robust retina-probe interface for high-quality, multi-neuron spike detection.
To create a hybrid bioelectronic system for image and object recognition using retinal signals.
To evaluate the system's performance in terms of spatial resolution, color/intensity recognition, and long-term stability.

Main Methods:

Development of a perforated and flexible microelectrode array to improve retina-probe interface.
Integration of the microelectrode array with retinal tissue to form a hybrid bioelectronic system.
Application of machine learning for image recognition tasks using detected neural signals.
Ex vivo testing of the system's accuracy and stability over time.

Main Results:

Achieved high-quality detection of spike firings from hundreds of neurons.
Demonstrated the system's capability for spatial resolution, color, and light intensity recognition.
Maintained over 94% accuracy in distinguishing light on/off conditions for 9 hours ex vivo.
Successfully developed a bioelectronic mimic eye for real-world object recognition.

Conclusions:

The developed perforated and flexible microelectrode array provides a robust interface for retinal spike recording.
The hybrid bioelectronic system effectively leverages retinal light-sensing for image and object recognition.
This technology shows significant potential for advancing visual prosthetics and understanding visual processing.