Biological Intelligence as a Computational Paradigm: Lessons from Living Systems for Next-Generation AI

Cliff Johnston

Biological Intelligence as a Computational Paradigm: Lessons from Living Systems for Next-Generation AI

Author(s)	Cliff Johnston
Country	United States
Abstract	The limitations of conventional artificial intelligence have intensified interest in computational paradigms inspired by the adaptive, distributed and self-organising properties of living systems. Biological organisms continuously process information, adapt to changing environments, learn from experience, regulate internal states and coordinate complex behaviours despite limited computational resources. These capabilities provide important principles for developing next-generation artificial intelligence systems. This paper examines biological intelligence as a computational paradigm and investigates how mechanisms observed in living systems can inform the design of adaptive, embodied, energy-efficient and resilient AI. Particular attention is given to neural computation, cellular intelligence, collective behaviour, evolutionary adaptation, predictive regulation, homeostasis, morphological computation and biological memory. The paper proposes a conceptual framework in which future AI systems integrate distributed information processing, embodied interaction, self-organisation, continual learning and adaptive resource allocation. The study further explores applications in autonomous robotics, edge intelligence, healthcare, environmental monitoring, swarm systems and adaptive manufacturing. A comparative analysis highlights fundamental differences between conventional AI architectures and biological intelligence, particularly in energy efficiency, robustness, contextual learning and adaptation under uncertainty. The paper also discusses challenges including the difficulty of translating biological mechanisms into computational architectures, limited theoretical understanding of intelligence in non-neural organisms and the trade-offs between biological fidelity and engineering practicality. It argues that the objective of bio-inspired AI should not be to reproduce biological systems literally, but to extract general computational principles from them. The convergence of neuroscience, synthetic biology, evolutionary computation, robotics and artificial intelligence could therefore establish a new generation of computational systems capable of learning, adapting and operating effectively in complex and unpredictable environments.
Keywords	: Biological Intelligence, Artificial Intelligence, Bio-Inspired Computing, Adaptive Systems, Neuromorphic Computing, Collective Intelligence, Evolutionary Computation, Embodied Intelligence, Self-Organisation, Next-Generation AI.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-13

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American Journal of Advanced Multidisciplinary Research and Innovation

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Biological Intelligence as a Computational Paradigm: Lessons from Living Systems for Next-Generation AI

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