Neuromorphic Network

Neuromorphic networks are artificial neural networks designed to mimic the brain's energy-efficient, event-driven processing. Current research focuses on developing efficient spiking neural network (SNN) architectures, often employing biologically-inspired neuron models like leaky integrate-and-fire neurons, and exploring training algorithms such as surrogate gradient methods and Hebbian learning, particularly for applications in image and video processing. These networks show promise in low-power computing, particularly for tasks like image recognition and semantic segmentation, demonstrating competitive performance with conventional deep learning models while significantly reducing computational costs. The use of novel hardware implementations, such as those based on magnetic tunnel junctions or superconducting circuits, further enhances their potential for energy-efficient, high-speed computation.