Neuromorphic Computing

Neuromorphic computing aims to build energy-efficient computer systems inspired by the brain's architecture, focusing on spiking neural networks (SNNs) that process information via brief electrical pulses. Current research emphasizes improving SNN training methods, particularly addressing challenges in deep network architectures and exploring novel algorithms like surrogate gradients and online learning rules to enhance accuracy and reduce power consumption. This field holds significant promise for advancing artificial intelligence, particularly in resource-constrained applications like robotics and edge computing, by offering substantial improvements in energy efficiency and processing speed compared to traditional computing.