Local Learning Rule

Local learning rules in artificial neural networks aim to develop more biologically plausible and computationally efficient training methods compared to traditional backpropagation. Current research focuses on developing such rules for various architectures, including spiking neural networks and deep neural networks, often inspired by biological mechanisms like Hebbian learning and spike-timing-dependent plasticity. These efforts are driven by the need for energy-efficient on-device learning and improved interpretability of neural network decisions, with applications ranging from edge computing to neuromorphic hardware. The ultimate goal is to create more robust, efficient, and understandable AI systems.