Learning in biological multilayer neuronal networks offers insights that extend beyond the classical weighted-sum neuron model commonly used in artificial neural networks. This article presents an accessible guide to a mechanistic neuronal network model that more accurately captures aspects of biological computation while enabling a simple yet powerful mechanism for learning in multilayer neural networks. The proposed approach supports efficient online streamed learning and provides a practical alternative to backpropagation. We demonstrate its potential in an image classification task, achieving competitive classification performance. The approach's simplicity, biological grounding, and broad applicability highlight a promising path toward algorithms that unify mechanistic neuron models and machine learning.

翻译：生物多层神经元网络的学习机制为人工神经网络中常用的经典加权和神经元模型提供了超越其局限的洞见。本文为一种更具生物计算特征的机制性神经元网络模型提供了通俗易懂的指南，该模型在多层神经网络中实现了既简单又强大的学习机制。所提出的方法支持高效的在线流式学习，并为反向传播提供了实用的替代方案。我们在图像分类任务中展示了其潜力，取得了具有竞争力的分类性能。该方法兼具简洁性、生物学基础与广泛适用性，为统一机制性神经元模型与机器学习算法的研究方向展现了颇具前景的途径。