  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.
