BIO-INTELLIGENCE FRAMEWORK

The Problem:
Current neural simulations ignore the cost of thinking. They learn patterns but not how biology stays alive while doing it.

The Solution

BIF rebuilds neural computation from physics upward. Each virtual neuron obeys TFIF geometry and carries an explicit ULE ledger for its metabolic cost. Plasticity, inhibition, astrocytic support, and sleep cycles emerge naturally.

Core Features

• ULE-accounted neurons: every spike equals quantifiable work and heat.
  
• Tripartite synapse model: includes astrocyte modulation and field-based learning.
  
• Self-healing networks: STDP + homeostasis recreate biological recovery.
  

Demonstrated Applications

• Neuro-health research: model stroke, sleep, and drug effects safely in silico.
  
• Energy-aware AI: networks that learn faster and forget safely.
  
• Cognitive-architecture testing: prototype neuromorphic chips before fabrication.
  

Impact:
Bridges neuroscience, AI, and hardware design—turning “bio-inspired computing” into bio-verified computing.