NeuroPlastic: A Plasticity-Modulated Optimizer for Biologically Inspired Learning Dynamics

2026-04-30 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

NeuroPlastic is a novel optimizer for deep learning that enhances traditional gradient-based updates with a multi-signal modulation mechanism inspired by neurobiological synaptic plasticity. Developed by researchers from Harvard Medical School and Harvard University, this optimizer dynamically scales gradient updates using interacting components that capture gradient, activity-like, and memory-like statistics. It functions as a lightweight modulation layer compatible with standard deep learning training pipelines, requiring no additional trainable parameters or architectural modifications. Evaluations on image classification benchmarks, including MNIST and Fashion-MNIST, show NeuroPlastic consistently improves performance over a controlled gradient-only ablation, with more significant gains observed on Fashion-MNIST and in reduced-data regimes. In transfer experiments on CIFAR-10 with ResNet-18, NeuroPlastic remains stable and competitive without retuning, suggesting its utility when learning signals are limited or noisy.

Key takeaway

For AI Engineers developing models in data-scarce or noisy environments, consider integrating NeuroPlastic into your training pipeline. This optimizer provides consistent performance improvements over gradient-only methods by leveraging biologically inspired multi-signal plasticity, without requiring architectural changes or additional trainable parameters. Its stability across different datasets, even without retuning, makes it a practical drop-in alternative to conventional optimizers like Adam or SGD when primary learning signals are weak.

Key insights

Multi-signal plasticity modulation can enhance gradient-based deep learning optimization, especially with limited or noisy data.

Principles

• Learning signals from multiple interacting sources are more informative than gradient information alone.
• Biologically inspired plasticity can be implemented as a lightweight, compatible modulation layer.
• Stabilization mechanisms are crucial for maintaining stable optimization dynamics with plasticity modulation.

Method

NeuroPlastic computes a per-parameter plasticity coefficient from normalized gradient magnitude, an exponential moving average of gradient activity, and an Adam-style memory term, then applies a homeostatic stabilization operator before updating parameters.

In practice

• Integrate multi-signal modulation into existing gradient-based optimizers.
• Prioritize NeuroPlastic in low-data or noisy learning environments.
• Utilize homeostatic stabilization to prevent exploding updates.

Topics

• NeuroPlastic Optimizer
• Synaptic Plasticity
• Deep Learning Optimization
• Multi-Signal Modulation
• Gradient-Based Learning

Code references

• Sunnylincc/NeuroPlastic-Optimizer

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

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