Learning Principles for Overcoming Non-ideal Factors in Brain

TL;DR

This paper argues that the brain's non-ideal features like noise and heterogeneity are essential for its robustness and adaptability, challenging traditional idealized models and highlighting the need for new learning principles.

Contribution

It introduces novel learning principles that leverage the brain's inherent non-ideal factors, moving beyond classical idealized computational models.

Findings

Non-ideal factors are evolutionary adaptations for robustness.

Classical models fail to capture brain's complexity and dynamics.

Understanding non-idealities can inspire new computational approaches.

Abstract

The human brain's computational prowess emerges not despite but because of its inherent "non-ideal factors"-noise, heterogeneity, structural irregularities, decentralized plasticity, systemic errors, and chaotic dynamics-challenging classical neuroscience's idealized models. These traits, long dismissed as flaws, are evolutionary adaptations that endow the brain with robustness, creativity, and adaptability. Classical frameworks falter under the brain's complexity: simulating 86 billion neurons and 100 trillion synapses is intractable, stochastic neurotransmitter release confounds signal interpretation, and the absence of global idealized models invalidates deterministic learning frameworks. Technological gaps further obscure whole-brain dynamics, revealing a disconnect between biological reality and computational abstraction.