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 **NEW in v3 - Cryptographic Applications:**- 11D Hypercube Reservoir passes ALL 9 NIST SP 800-22 randomness tests- 65% fewer connections than random sparse networks with equivalent security- Sequence length independence: 10-16% improvement across 10-500 character sequences- First demonstration of brain-like topology achieving cryptographic-grade randomness 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× capacity2. Phase Coding: 10^10× capacity3. 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, high-dimensional topology, and structured connectivity in brain-inspired computing systems, with reproducible Python simulations. GitHub: https://github.com/hafufu-stack/brain-vs-neumann