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

We present a comprehensive quantitative comparison between von Neumann and brain-like computing architectures, focusing on information capacity, energy efficiency, and fault tolerance. Key findings:- 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 Four coding schemes compared:1. Rate Coding: 2.5×10^10× capacity2. 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 in brain-inspired computing systems, with reproducible Python simulations. GitHub: https://github.com/hafufu-stack/brain-vs-neumann