Von Neumann vs Brain-like Architecture: A Quantitative Comparison of Information Capacity and Efficiency

I present a comprehensive quantitative comparison between von Neumann and brain-like computing architectures, focusing on information capacity, energy efficiency, and fault tolerance.

**Key findings (v1):**
- Information Capacity: Brain-like architecture achieves up to 9.3×10^13× more capacity using burst coding (phase + inter-spike interval)
- Energy Efficiency: 100× more efficient due to elimination of bus transfers
- Fault Tolerance: Graceful degradation up to 30% component failure
- Real-World Tasks: 4/4 correct vs 3/4 for von Neumann

**NEW in v2 - 11-Dimensional Brain Structure:**
- Based on Blue Brain Project's discovery of 11D clique structures in the brain
- 11D Hypercube achieves 8× faster information propagation than 2D grid
- MNIST 10-digit classification: 9D Hypercube achieves 90.6% accuracy (best)
- Base-10 shows maximum advantage (+4.2%) for hypercube topology
- Parameter efficiency: 186× reduction compared to full connection

This may explain why humans naturally developed the base-10 numeral system: 10 fingers + 11-dimensional brain = optimal for 10 categories.

Four coding schemes compared:
1. Rate Coding: 2.5×10^10× capacity
2. Phase Coding: 10^10× capacity
3. Burst Coding: 9.3×10^13× capacity (novel finding)
4. Correlation Coding: 10^9× capacity

This work provides theoretical foundation for the advantages of temporal coding and high-dimensional topology in brain-inspired computing systems, with reproducible Python simulations.

GitHub: https://github.com/hafufu-stack/brain-vs-neumann