LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization

Marco P. E. Apolinario, Arani Roy, Kaushik Roy; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7796-7805

Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption particularly for on-device learning where computational resources are limited. Various alternatives to BP including random feedback alignment forward-forward and local classifiers have been explored to address these challenges. These methods have their advantages but they can encounter difficulties when dealing with intricate visual tasks or demand considerable computational resources. In this paper we propose a novel Local Learning rule inspired by neural activity Synchronization phenomena (LLS) observed in the brain. LLS utilizes fixed periodic basis vectors to synchronize neuron activity within each layer enabling efficient training without the need for additional trainable parameters. We demonstrate the effectiveness of LLS and its variations LLS-M and LLS-MxM on multiple image classification datasets achieving accuracy comparable to BP with reduced computational complexity and minimal additional parameters. Specifically LLS achieves comparable performance with up to 300x fewer multiply-accumulate (MAC) operations and half the memory requirements of BP. Furthermore the performance of LLS on the Visual Wake Word (VWW) dataset highlights its suitability for on-device learning tasks making it a promising candidate for edge hardware implementations.