Emerging Trends in Bio-Inspired Computing Models

Bio-inspired computing models are computational approaches inspired by biological systems such as evolution, neural processing, swarm behavior, immune systems, and cellular communication. They are widely used for optimization, learning, adaptation, and control in complex environments. Recent advances in AI, edge computing, healthcare, smart cities, and autonomous systems have increased interest in these models due to their adaptability, robustness, and energy efficiency. Current research focuses on evolutionary computing, swarm intelligence, neural and spiking neural networks, immune-inspired computing, and hybrid approaches that combine multiple biological principles. Emerging technologies such as neuromorphic computing, memristive hardware, and analog in-memory computing further support low-power intelligent systems. This work presents a unified framework based on biological mechanisms, computational objectives, deployment contexts, and performance constraints, demonstrating its application in edge-IoT anomaly detection and resource management. Overall, bio-inspired computing is emerging as a practical and effective paradigm for developing adaptive, distributed, and energy-efficient intelligent systems.