MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC

2026-05-05 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

MPCS (Multi-Plasticity Continual System) is a neuroplastic continual learning architecture that integrates eleven complementary mechanisms to balance acquiring new knowledge (plasticity) and retaining prior knowledge (stability). The system was evaluated on MEP-Bench, a multi-track benchmark with 31 tasks across regression, classification, logic, and mixed domains. MPCS achieved a Normalized Efficiency Score (NES) of 94.2, placing it on the Pareto frontier among 9 of 14 gate-passing systems. Key findings from a 15-configuration ablation study, totaling over $7,800 GPU minutes, revealed that Fourier encoding is the most critical component, while global EWC regularization degrades performance. A more efficient variant, mpcs_efficient, which disables EWC and Hebbian updates, achieved higher performance (Perf = 0.9086) at 4.7x lower compute cost (127 minutes vs. 602 minutes).

Key takeaway

For research scientists developing continual learning systems, you should critically evaluate the interaction effects of multiple plasticity mechanisms rather than relying on single dominant approaches. The finding that Pareto-dominated components can be jointly removed to yield a faster, more accurate system (mpcs_efficient) suggests that your ablation studies should incorporate multi-objective optimization to identify truly dispensable components and optimize for both performance and compute efficiency.

Key insights

Integrating multiple plasticity mechanisms and using Pareto analysis can optimize continual learning systems.

Principles

• Fourier encoding is critical for input manifold smoothing.
• Global EWC can degrade performance due to cross-task Fisher corruption.
• Pareto analysis identifies jointly dispensable components for efficiency.

Method

MPCS integrates eleven plasticity mechanisms, including task-driven neurogenesis, Fourier-encoded inputs, and meta-replay, evaluated using a three-dimensional Pareto criterion (Perf, RD, GCR) on MEP-Bench.

In practice

• Prioritize Fourier encoding for input processing.
• Avoid global EWC; consider topology-local variants.
• Use Pareto analysis to guide model compression.

Topics

• Neuroplastic Continual Learning
• Multi-Component Plasticity
• Fourier Input Encoding
• Topology-Aware EWC
• MEP-Bench Benchmark

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.