Advances in neural computation have predominantly relied on the gradient backpropagation algorithm (BP). However, the recent shift towards non-stationary data modeling has highlighted the limitations of this heuristic, exposing that its adaptation capabilities are far from those seen in biological brains. Unlike BP, where weight updates are computed through a reverse error propagation path, Hebbian learning dynamics provide synaptic updates using only information within the layer itself. This has spurred interest in biologically plausible learning algorithms, hypothesized to overcome BP's shortcomings. In this context, Hinton recently introduced the Forward-Forward Algorithm (FFA), which employs local learning rules for each layer and has empirically proven its efficacy in multiple data modeling tasks. In this work we argue that when employing a squared Euclidean norm as a goodness function driving the local learning, the resulting FFA is equivalent to a neo-Hebbian Learning Rule. To verify this result, we compare the training behavior of FFA in analog networks with its Hebbian adaptation in spiking neural networks. Our experiments demonstrate that both versions of FFA produce similar accuracy and latent distributions. The findings herein reported provide empirical evidence linking biological learning rules with currently used training algorithms, thus paving the way towards extrapolating the positive outcomes from FFA to Hebbian learning rules. Simultaneously, our results imply that analog networks trained under FFA could be directly applied to neuromorphic computing, leading to reduced energy usage and increased computational speed.

翻译：神经计算领域的进展主要依赖于梯度反向传播算法（BP）。然而，最近向非平稳数据建模的转变凸显了这种启发式方法的局限性，揭示其适应能力远不及生物大脑所展现的水平。与通过反向误差传播路径计算权重更新的BP不同，赫布学习动力学仅利用层内信息提供突触更新。这激发了人们对具有生物合理性的学习算法的兴趣，这些算法被假设能够克服BP的缺陷。在此背景下，Hinton近期提出了前向-前向算法（FFA），该算法为每一层采用局部学习规则，并已在多项数据建模任务中经验证明确实有效。本文认为，当采用平方欧几里得范数作为驱动局部学习的优度函数时，所得的FFA等价于一种新赫布学习规则。为验证这一结果，我们比较了FFA在模拟网络中的训练行为与其在脉冲神经网络中的赫布适应版本。实验表明，两种FFA版本均产生相似的准确性和潜在分布。本文报告的研究结果为连接生物学习规则与当前使用的训练算法提供了经验证据，从而为将FFA的积极成果推广至赫布学习规则铺平了道路。同时，我们的结果意味着，在FFA下训练的模拟网络可直接应用于神经形态计算，从而实现能耗降低与计算速度提升。