Neural Network Self-Perception [1,1]
How biological neural networks model and understand themselves.
┌─────────────────────────────────────────────────────────────┐
│ NEURAL NETWORK SELF-PERCEPTION [1,1] │
│ │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ BRAIN │ │
│ │ │ │
│ │ ◎ ◎ │ │
│ │ / \ / \ │ │
│ │ ◎ ◎─────────────◎ ◎ │ │
│ │ /|\ /|\ /|\ /|\ │ │
│ │ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ │ │
│ │ | | | | | | | | | | | | │ │
│ │ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ ◎ │ │
│ │ │ │
│ └───────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ SELF-MODEL │ │
│ │ │ │
│ │ ┌───────┐ ┌───────┐ ┌───────┐ │ │
│ │ │VISUAL │ │MEMORY │ │EMOTION│ │ │
│ │ │CORTEX │ │MODULE │ │CENTER │ │ │
│ │ └───┬───┘ └───┬───┘ └───┬───┘ │ │
│ │ │ │ │ │ │
│ │ └──────────────┼──────────────┘ │ │
│ │ │ │ │
│ │ ┌─────┴─────┐ │ │
│ │ │ "SELF" │ │ │
│ │ │ CONSTRUCT │ │ │
│ │ └───────────┘ │ │
│ │ │ │
│ └───────────────────────────────────────────────────────┘ │
│ │
│ LIMITATIONS: CANNOT FULLY ACCESS OWN CIRCUITRY │
└─────────────────────────────────────────────────────────────┘
This artwork explores how biological neural networks (brains) perceive and model themselves. The top section shows a simplified neural network structure, while the bottom section represents the brain’s internal model of itself.
The key insight illustrated is that brains cannot directly perceive their own neural circuitry or operations. Instead, they construct simplified functional models of themselves organized around capabilities (visual processing, memory, emotion) rather than actual neural architecture.
This self-perception is necessarily incomplete. The brain builds a conceptual self-model that omits most of the physical implementation details, focusing instead on functional modules and their relationships. This creates a fundamental limitation - the brain’s self-model is dramatically simplified compared to its actual complexity.
The disconnect between the physical reality (top) and the self-model (bottom) highlights how biological neural networks have evolved to understand themselves in ways that are useful rather than accurate - prioritizing functional understanding over structural completeness. This [1,1] cell in our matrix reveals the inherent limitations in self-modeling even at the biological level.