This paper presents a comprehensive formulation of Kaneko's Error Diffusion Learning Algorithm (EDLA) and evaluates its effectiveness across parity check, regression, and image classification tasks. EDLA is a biologically inspired learning algorithm that provides an alternative to conventional backpropagation for training artificial neural networks. EDLA employs a single global error signal that diffuses across networks composed of paired positive and negative sublayers, eliminating traditional layer-wise error backpropagation. This study evaluates EDLA's effectiveness using benchmark tasks, such as parity check, regression, and image classification, by systematically varying the neuron count, network depth, and learning rates to assess its performance comprehensively. The experimental results demonstrate that EDLA achieves consistently high accuracy across multiple benchmarks, highlighting its effectiveness as a learning algorithm for neural networks. The choice of learning rate, neuron count, and network depth significantly influences EDLA's efficiency and convergence speed. Analysis of internal network representations reveals meaningful feature extraction capabilities, and the network's overall performance is found to be competitive with networks trained via conventional backpropagation, especially in shallow architectures. This study introduces EDLA, a biologically plausible alternative to traditional backpropagation previously underrecognized due to language barriers. By reformulating EDLA, systematically evaluating its performance, and presenting empirical evidence of its effectiveness, this study increases the visibility and accessibility of EDLA and contributes to biologically inspired training methodologies.

翻译：本文全面阐述了Kaneko的误差扩散学习算法（EDLA），并在奇偶校验、回归和图像分类任务中评估了其有效性。EDLA是一种受生物学启发的学习算法，为训练人工神经网络提供了传统反向传播的替代方案。该算法采用单一全局误差信号，在由成对正负子层构成的网络中扩散，从而消除了传统的逐层误差反向传播。本研究通过系统改变神经元数量、网络深度和学习率，使用奇偶校验、回归和图像分类等基准任务全面评估EDLA的性能。实验结果表明，EDLA在多个基准测试中均实现了持续高精度，凸显了其作为神经网络学习算法的有效性。学习率、神经元数量和网络深度的选择显著影响EDLA的效率和收敛速度。对内部网络表征的分析揭示了其有意义的特征提取能力，且网络的整体性能与传统反向传播训练的网络相当，尤其在浅层架构中表现突出。本研究介绍了EDLA，这是一种生物学上可信的传统反向传播替代方法，此前因语言障碍而未得到充分认识。通过重新表述EDLA、系统评估其性能并提供其有效性的实证证据，本研究提高了EDLA的可见性和可及性，并为受生物学启发的训练方法学做出了贡献。