AN INNOVATIVE ENGINEERING FRAMEWORK FOR SOPHISTICATED AUTONOMOUS SYSTEMS UTILIZI,NG BRAIN-INSPIRED COMPUTATIONAL CONCEPTS
Keywords:
neuromorphic architecture, spiking neural models, quantum-derived neuronal models, Event-driven attentional selection, Adaptive resonance mechanisms, Sequential memory hierarchies, Structural plasticity, spiking transformer networks, Dynamic precision scaling, Power efficiencyAbstract
A comprehensive neuromorphic architecture that integrates seven neurobiological and quantum-inspired approaches is presented to address ongoing limitations in spiking neural models (Davies et al., 2018; Merolla et al., 2014). This framework includes quantum-derived neuronal models (Narayanan & Menneer, 2000), event-driven attentional selection (Vaswani et al., 2017), adaptive resonance mechanisms (Grossberg, 1976), sequential memory hierarchies (Hawkins & Ahmad, 2016), structural plasticity (Yang et al., 2013), spiking transformer networks, and dynamic precision scaling. Benchmark tests indicate improvements of 3.5× in power efficiency, 2× in training speed, and 2.2× in adaptive response compared to traditional spiking systems (Wang et al., 2020; Zenke & Ganguli, 2018). Empirical validation in the domains of robotic control and anomaly detection achieved a detection accuracy of 63.3%, while preserving neurophysiological fidelity. This research advances a unified intelligence model, combining neuromorphic engineering (Chklovskii et al., 2004) with machine learning to facilitate sophisticated autonomous and edge computing platforms.
