Metaplasticity as adaptive gradient preconditioning for incremental learning
Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses. While artificial neural networks struggle with the stability-plasticity dilemma in non-stationary environments, existing solutions often require task labels or incur massive memory overhead, diverging from biological reality. Re-framing this localized neuromodulation as an optimization-driven process, we introduce $\textbf{SynGAP}$: $\textbf{Syn}$aptic $\textbf{G}$eometric $\textbf{A}$daptive $\textbf{P}$recon...
| Authors | Isabelle Aguilar, Zayn Andre Zainal, Omid Kavehei |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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