NEUROPLASTIC: A PLASTICITY-MODULATED OPTIMIZER FOR BIOLOGICALLY INSPIRED LEARNING DYNAMICS

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 aug- ments gradient-based updates with an adaptive multi-signal modulation mech- anism inspired by multi-factor synaptic plasticity, a concept from neurobiol- ogy. NeuroPlastic dynamically scales gradient updates using interacting com- ponents 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 im- proves over a controlled gradient-only ablation, with more pronounced gains on the Fashion-MNIST benchmark and in reduced-data regimes. In transfer experi- ments on CIFAR-10 with ResNet-18, the method remains stable and competitive without retuning. These results suggest that multi-signal plasticity-inspired mod- ulation can provide a useful extension to conventional gradient-driven optimiza- tion, particularly when learning signals are limited or noisy, and offer a promis- ing direction for gradient-based methods in deep learning.