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

2026-04-29

AI Summary

This work proposes NeuroPlastic, a novel optimizer that addresses the limitations of conventional deep learning optimizers, which rely solely on local gradients and lack inspiration from biological neural plasticity, often underperforming under data scarcity or noisy conditions. NeuroPlastic is the first to incorporate multi-factor synaptic plasticity mechanisms from neuroscience into the optimization process, integrating gradient, activity-like, and memory-like signals through a lightweight adaptive modulation layer. This layer dynamically adjusts parameter updates without altering standard training pipelines. Compatible with mainstream deep learning frameworks, NeuroPlastic consistently outperforms baseline optimizers on Fashion-MNIST and few-shot image classification tasks and demonstrates strong generalization in CIFAR-10 transfer experiments—all without requiring re-tuning of hyperparameters.

Abstract

Optimization algorithms are fundamental to modern deep learning, yet most widely used methods rely on update rules based primarily on local gradient statistics. We introduce NeuroPlastic, a plasticity-modulated optimizer that augments gradient-based updates with an adaptive multi-signal modulation mechanism inspired by multi-factor synaptic plasticity, a concept from neurobiology. NeuroPlastic dynamically scales gradient updates using interacting components that capture gradient, activity-like, and memory-like statistics, forming a lightweight modulation layer compatible with standard deep learning training pipelines. Across image classification benchmarks, NeuroPlastic consistently improves over a controlled gradient-only ablation, with more pronounced gains on the Fashion-MNIST benchmark and in reduced-data regimes. In transfer experiments on CIFAR-10 with ResNet-18, the method remains stable and competitive without retuning. These results suggest that multi-signal plasticity-inspired modulation can provide a useful extension to conventional gradient-driven optimization, particularly when learning signals are limited or noisy, and offer a promising direction for gradient-based methods in deep learning.

Problem

• optimization
• synaptic plasticity
• gradient-based learning
• biologically inspired
• deep learning

Innovation

• plasticity-modulated optimizer
• multi-signal modulation
• synaptic plasticity
• gradient-based optimization
• biologically inspired learning