Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs. Despite its pivotal role in propelling deep learning advancements, the biological plausibility of backpropagation is questioned due to its requirements for weight symmetry, global error computation, and dual-phase training. To address this long-standing challenge, many studies have endeavored to devise biologically plausible training algorithms. However, a fully biologically plausible algorithm for training multilayer neural networks remains elusive, and interpretations of biological plausibility vary among researchers. In this study, we establish criteria for biological plausibility that a desirable learning algorithm should meet. Using these criteria, we evaluate a range of existing algorithms considered to be biologically plausible, including Hebbian learning, spike-timing-dependent plasticity, feedback alignment, target propagation, predictive coding, forward-forward algorithm, perturbation learning, local losses, and energy-based learning. Additionally, we empirically evaluate these algorithms across diverse network architectures and datasets. We compare the feature representations learned by these algorithms with brain activity recorded by non-invasive devices under identical stimuli, aiming to identify which algorithm can most accurately replicate brain activity patterns. We are hopeful that this study could inspire the development of new biologically plausible algorithms for training multilayer networks, thereby fostering progress in both the fields of neuroscience and machine learning.

翻译：反向传播是训练神经网络进行监督学习的核心算法，它采用梯度下降方法，通过最小化实际输出与期望输出之间的差异来更新网络权重。尽管反向传播在推动深度学习进步中发挥着关键作用，但其对权重对称性、全局误差计算和双阶段训练的要求使其生物合理性受到质疑。为应对这一长期存在的挑战，许多研究致力于设计生物合理的学习算法。然而，完全适用于多层神经网络训练的生物合理算法仍未实现，且研究者对生物合理性的解释各不相同。在本研究中，我们建立了一套理想学习算法应满足的生物合理性标准。基于这些标准，我们评估了一系列现有被认为具有生物合理性的算法，包括Hebbian学习、脉冲时序依赖可塑性、反馈对齐、目标传播、预测编码、前向-前向算法、扰动学习、局部损失和基于能量的学习。此外，我们在不同网络架构和数据集上对这些算法进行了实证评估。我们将这些算法学习到的特征表示与在相同刺激下通过非侵入式设备记录的大脑活动进行比较，旨在确定哪种算法能最准确地复现大脑活动模式。我们希望这项研究能够启发开发新的生物合理算法用于训练多层网络，从而促进神经科学和机器学习领域的共同进步。