通过调整神经元连接系数实现无需链式求导的神经网络训练
A Neural Network Training Method Based on Neuron Connection Coefficient Adjustments
- 基于反向信号传播，直接修改神经元间连接系数
- 在MNIST上取得良好效果，避免部分局部极小值
- 生物可解释性强，适合对机制透明性要求高的研究
此前研究提出了一种基于对称微分方程的神经网络框架及其一种训练方法。本文提出该框架的另一种训练策略，利用反向信号传播，无需依赖传统的链式求导法则，具有较强的生物可解释性。与之前方法不同，该策略不需调整微分方程的固定点，仅通过修改神经元间的连接系数实现训练，与传统多层感知机（MLP）的训练过程高度相似。采用合适的调整策略后，该方法能有效避开某些潜在的局部极小值。在MNIST数据集上的实验验证了其有效性。进一步分析揭示了当前神经网络架构的部分局限性，并提出了改进措施。
原文摘要 · Abstract (English)
In previous studies, we introduced a neural network framework based on symmetric differential equations, along with one of its training methods. In this article, we present another training approach for this neural network. This method leverages backward signal propagation and eliminates reliance on the traditional chain derivative rule, offering a high degree of biological interpretability. Unlike the previously introduced method, this approach does not require adjustments to the fixed points of the differential equations. Instead, it focuses solely on modifying the connection coefficients between neurons, closely resembling the training process of traditional multilayer perceptron (MLP) networks. By adopting a suitable adjustment strategy, this method effectively avoids certain potential local minima. To validate this approach, we tested it on the MNIST dataset and achieved promising results. Through further analysis, we identified certain limitations of the current neural network architecture and proposed measures for improvement.
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