Learning is Architecture: From Backpropagation's Structural Flaw to Nested Co-Adaptation Across Timescales

Key Points

• This research aims to redefine learning by identifying structural flaws in backpropagation and presenting a framework for understanding adaptation across different layers.
• Developed a theoretical framework for learning based on physical architecture adaptation.
• Identified four nested layers of adaptation: evolution, development, plasticity, and dynamics.
• Conducted MNIST experiments to validate the proposed theory.
• Validated the theoretical framework through MNIST experiments demonstrating its effectiveness.
• Outlined implications for understanding memory, disease, trauma, pain, and consciousness based on the new learning theory.

Abstract