Neuromorphic Computing for AI: Bridging Artificial and Biological Neural Networks Through Spiking Models

Neuromorphic computing is an emerging transformative approach inspired by the structure and functioning of biological neural networks toward enhancing the efficiency and adaptability of artificial intelligence systems. This work explores the use of spiking neural networks (SNNs), memristive devices and novel two-dimensional materials to advance neuromorphic computing technologies. We analyzed these systems using comparative experimental evaluation and discussed applications such as remote sensing scene classification and sensory processing based on recent neuromorphic computing studies. Results indicated reduced inference latency characteristics for SNN-based neuromorphic systems compared with conventional neural architectures under equivalent evaluation settings. The biomimetic neuromorphic sensory system based on electrolyte-gated transistors demonstrated approximately 15% lower estimated energy consumption compared with conventional processing approaches reported in related studies. Memristive neuromorphic devices demonstrated improved synaptic adaptation characteristics, indicating enhanced learning and plasticity behavior in comparison with conventional implementations reported in related studies. The study further discusses future prospects of neuromorphic computing using advanced materials and adaptive architectures.