Self-evolving cognitive substrates through metabolic data processing and recursive self-representation with...

Area of Science:

• Artificial Intelligence
• Computational Neuroscience
• Biomimetic Computing

Background:

• Conventional artificial intelligence (AI) systems possess static architectures requiring periodic retraining, limiting their adaptability in dynamic data environments.
• This necessitates a paradigm shift towards AI systems capable of continuous learning and autonomous adaptation.

Purpose of the Study:

• To introduce a novel biologically inspired computing paradigm for perpetual AI learning.
• To enable lifelong learning, self-repair, and adaptive evolution in AI systems without human intervention.

Main Methods:

• Development of dynamic cognitive substrates for continuous data assimilation and uninterrupted learning.
• Integration of quantum-inspired uncertainty management for robustness and biomimetic self-healing for structural integrity.
• Implementation of fractal propagation for micro-optimization and recursive learning mechanisms for functional refinement.

Main Results:

• The proposed architecture demonstrated sustained effective learning across diverse, heterogeneous data domains.
• The system autonomously restructured itself, maintaining stability and improving performance in dynamic environments.
• Specialized cognitive processing units and evolutionary information prioritization mimicked biological memory consolidation.

Conclusions:

• Continuous, self-modifying AI architectures outperform traditional models in non-stationary conditions.
• The integration of quantum uncertainty control, biomimetic repair, and fractal optimization enables resilient, autonomous lifelong learning.
• This approach paves the way for fully autonomous, continuously learning artificial organisms.