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A Machine Learning-Based Decoder Framework for the Cortical Voltage-Sensitive Dye Responses to Retinal Neuromorphic

Keisuke Yamada¹, Yuina Terakura¹, Santa Fukuda¹

¹Department of Information Engineering, Graduate School of Engineering, Mie University, Tsu 514-8507, Japan.

Bioengineering (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

This study demonstrates that machine learning can decode visual information from simulated cortical responses to intracortical microstimulation (ICMS). This computational approach validates the potential of using neuromorphic spike trains for visual prostheses.

Keywords:

intracortical visual prosthesis machine learning-based decoding physiological experiment retinal neuromorphic spike voltage-sensitive dye imaging

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Area of Science:

Neuroscience
Biomedical Engineering
Computational Vision

Background:

Intracortical microstimulation (ICMS) shows promise for visual prostheses.
Neuromorphic spike trains derived from images are proposed for ICMS pulse sequences.
Cortical voltage-sensitive dye (VSD) responses to ICMS require further investigation for image information content.

Purpose of the Study:

To explore the feasibility of machine-learning-based decoding of image information from cortical responses to ICMS.
To address the lack of large-scale experimental datasets linking visual images, spike trains, and cortical responses.

Main Methods:

Generated surrogate data using a Wiener-system model to simulate VSD responses to ICMS pulse trains.
Trained a convolutional neural network (CNN) on synthetic datasets.
Evaluated the CNN's ability to reconstruct images from simulated cortical responses.

Main Results:

A CNN successfully reconstructed images from simulated VSD responses.
The study demonstrated computational feasibility of decoding visual information from simulated cortical activity.
The findings provide a foundation for future physiological validation.

Conclusions:

Machine learning-based decoding of visual information from simulated cortical activity evoked by neuromorphic ICMS is computationally feasible.
This proof-of-concept study supports the potential of using neuromorphic ICMS for visual prostheses.
Further research is needed for physiological validation.