A Spike-Driven Hopfield Memory for Online Continual Learning

Associative memory is inspired by the brain’s ability to recover stored content from partial cues while incorporating new experiences over time. Hopfield networks provide a computational form of associative recall, yet many variants realise memory through state-based operations rather than spike-mediated neural dynamics. We therefore propose the Spike-driven Hopfield Online Neural Architecture (SHONA), which couples membrane dynamics, timing-dependent plasticity, and spike-driven forgetting control to reformulate Hopfield associative memory as a continual process in which memories are retrieved, updated, and regulated. Experiments show that our approach sustains associative recall while embedding online adaptation directly into the memory process.