  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. 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.
