Color Spike Data Generation via Bio-inspired Neuron-like Encoding with an Artificial Photoreceptor Layer

TL;DR

This paper introduces a bio-inspired neuron-like encoding method with an artificial photoreceptor layer to generate rich color and luminance spike data, enhancing the information capacity and performance of spiking neural networks while maintaining neuromorphic principles.

Contribution

The study presents a novel encoding approach that combines biological neuron principles with an artificial photoreceptor layer to improve spike data quality for neuromorphic computing.

Findings

Enhanced spike data with color and luminance information.

Improved SNN performance using the proposed encoding method.

Biologically inspired approach increases information content of spike signals.

Abstract

In recent years, neuromorphic computing and spiking neural networks (SNNs) have ad-vanced rapidly through integration with deep learning. However, the performance of SNNs still lags behind that of convolutional neural networks (CNNs), primarily due to the limited information capacity of spike-based data. Although some studies have attempted to improve SNN performance by training them with non-spiking inputs such as static images, this approach deviates from the original intent of neuromorphic computing, which emphasizes spike-based information processing. To address this issue, we propose a Neuron-like Encoding method that generates spike data based on the intrinsic operational principles and functions of biological neurons. This method is further enhanced by the incorporation of an artificial pho-toreceptor layer, enabling spike data to carry both color and luminance information, thereby forming a complete visual spike signal. Experimental results using the Integrate-and-Fire neuron model demonstrate that this biologically inspired approach effectively increases the information content of spike signals and improves SNN performance, all while adhering to neuromorphic principles. We believe this concept holds strong potential for future development and may contribute to overcoming current limitations in neuro-morphic computing, facilitating broader applications of SNNs.