Neurological Neural Networks (NNN)

Abstract

Neurological Neural Networks (NNN) is a bio-inspired theoretical framework that seeks to bridge biological neural computation and artificial intelligence through mathematically grounded modelling and hybrid neural architectures. The framework is motivated by the observation that biological nervous systems achieve remarkable adaptability, robustness, and energy efficiency through event-driven computation, temporal encoding, and synaptic plasticity, capabilities that remain limited in conventional artificial neural networks. NNN integrates spiking neural networks (SNNs), artificial neural networks (ANNs), and neuromorphic computing principles into a unified architecture for robotics and assistive technologies. By explicitly modelling neuron membrane dynamics, synaptic integration, and spike timing dependent plasticity, the framework enables continuous online learning, low-latency response, and energy-efficient computation. SNNs are employed for temporally precise sensory processing and motor execution, while ANNs provide higher-level abstraction, perception, and decision-making capabilities. This hybrid approach allows NNN to support adaptive robotic control, neuro-assisted rehabilitation systems, and human-centered robotic interaction. Rather than proposing a single algorithm, NNN serves as a theoretical and architectural foundation that can be implemented across diverse hardware platforms, including neuromorphic processors and conventional computing systems. The framework contributes toward the development of biologically plausible, scalable, and real-time assistive intelligent systems capable of operating in dynamic, real-life environments.