Brain Learning Principles Utilizing Non-Ideal Factors in Neural Circuits

TL;DR

This paper argues that the brain's non-ideal features like noise and irregularities are fundamental design principles that enhance its robustness, adaptability, and creativity, challenging traditional views of these traits as imperfections.

Contribution

It systematically shows that non-ideal factors in neural circuits are beneficial evolutionary features rather than flaws, offering new insights into brain function.

Findings

Non-ideal factors contribute to robustness and adaptability.

Irregularities support creativity and flexible processing.

Evolutionary design leverages imperfections for improved function.

Abstract

The human brain achieves its remarkable computational prowess not despite its inherent non-ideal factors noise, heterogeneity, structural irregularities, decentralized plasticity, systematic errors, and chaotic dynamics but precisely because of them. This paper systematically demonstrates that these traits, long dismissed as imperfections in classical neuroscience and eliminated in digital engineering, are evolutionary design principles that endow the brain with robustness, adaptability, and creativity.