Neuromorphic Engineering

Neuromorphic engineering aims to build energy-efficient computing systems inspired by the brain's architecture and information processing mechanisms. Current research heavily focuses on spiking neural networks (SNNs), often implemented using models like leaky integrate-and-fire neurons and trained with surrogate gradient methods, and exploring various architectures such as U-Nets and convolutional networks for tasks like image processing and robotic navigation. This field is significant for its potential to create low-power, high-performance computing hardware and to advance our understanding of biological intelligence through the development and testing of bio-inspired models.