Research on simulation of human brain neural network and signal processing technology

With the cross development of computational neuroscience and artificial intelligence, research on human brain neural network simulation and signal processing technology has become a hot topic of common concern in both academia and industry. This study used a multi-level and multi-scale computational model and high-precision algorithm to simulate the dynamic behavior of human brain neural networks, exploring the complex interactions between neurons and their impact on information processing capabilities. Real time dynamic simulation of billions of neurons was achieved on a simulation platform using ultra large-scale integrated circuit chips, simulating signal processing patterns similar to those of the human brain. By using an improved backpropagation algorithm to decode and reconstruct neural signals, the efficiency of the algorithm and the accuracy of signal processing have been improved. On this basis, combined with experimental data obtained from functional magnetic resonance imaging, the similarities and differences between neural network simulation results and actual human brain activity were compared and analyzed, revealing the potential connection between cognitive function and brain network activity patterns. This study not only achieved new breakthroughs in simulation technology and signal processing algorithms, but also provided a new quantitative tool and theoretical support for related neuroscience research, which is of great significance for the development of brain computer interfaces and intelligent information processing systems. In addition, the study also delved into the balance between ensuring model complexity and processing efficiency, as well as the challenges and opportunities brought by interdisciplinary collaboration in the field of neuroscience. Through cross validation and error analysis of simulation experiments, the effectiveness of the model and the accuracy of prediction results were ensured. Based on this, feasible suggestions for optimizing neural network structures and signal decoding strategies have been extracted, providing possible new avenues for future research directions.