Neuromorphic Artificial Intelligence

Neuromorphic artificial intelligence (AI) aims to build computing systems inspired by the brain's structure and function, primarily using spiking neural networks (SNNs) to process event-based data efficiently. Current research focuses on improving SNN training speed and accuracy through novel learning rate enhancements and data augmentation techniques, as well as developing energy-efficient hardware implementations using memristors and specialized neuron models like two-point cells. This field is significant for its potential to create low-power, robust AI systems for applications such as robotics and embedded systems, addressing limitations of traditional AI approaches in terms of energy consumption and adaptability.