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.

通过对神经计算的进一步研究，我们证明了当使用平方欧几里得范数作为驱动局部学习的好度函数时，Forward-Forward Algorithm（FFA）等同于新赫布学习规则，并通过比较两个版本的FFA在模拟网络和脉冲神经网络中的训练行为，证实了生物学习规则与当前使用的训练算法之间的关联，并为将FFA的积极结果推广到赫布学习规则铺平了道路。同时，我们的结果暗示在FFA下训练的模拟网络可以直接应用于神经形态计算，从而实现能源消耗的降低和计算速度的增加。

前向学习中新兴的NeoHebbian动态: 对神经形态计算的意义