A Backpropagation-Free Feedback-Hebbian Network for Continual Learning Dynamics

TL;DR

This paper demonstrates that a simple, backpropagation-free feedback-Hebbian neural network can support continual learning by regenerating representations and maintaining associations through local plasticity and dedicated feedback pathways.

Contribution

It introduces a minimal feedback-Hebbian architecture with local learning rules that effectively supports continual learning and representation regeneration without backpropagation.

Findings

Supports representation regeneration and co-maintenance of associations.

Feedback pathways preserve traces of prior associations during new learning.

Local plasticity rules enable continual adaptation in a minimal network.

Abstract

Feedback-rich neural architectures can regenerate earlier representations and inject temporal context, making them a natural setting for strictly local synaptic plasticity. Existing literature raises doubt about whether a minimal, backpropagation-free feedback-Hebbian system can already express interpretable continual-learning-relevant behaviors under controlled training schedules. In this work, we introduce a compact prediction-reconstruction architecture with a dedicated feedback pathway providing lightweight, locally trainable temporal context for continual adaptation. All synapses are updated by a unified local rule combining centered Hebbian covariance, Oja-style stabilization, and a local supervised drive where targets are available. With a simple two-pair association task, learning is characterized through layer-wise activity snapshots, connectivity trajectories (row and column means of learned weights), and a normalized retention index across phases. Under sequential A to B training, forward output connectivity exhibits a long-term depression (LTD)-like suppression of the earlier association, while feedback connectivity preserves an A-related trace during acquisition of B. Under an alternating sequence, both associations are concurrently maintained rather than sequentially suppressed. Architectural controls and rule-term ablations isolate the role of dedicated feedback in regeneration and co-maintenance, alongside the role of the local supervised term in output selectivity and unlearning. Together, the results show that a compact feedback pathway trained with local plasticity can support regeneration and continual-learning-relevant dynamics in a minimal, mechanistically transparent setting.