The Application of Complex-Valued Neural Networks in Neuroscience

This study investigates the application of complex-valued neural networks (CVNNs) in cognitive modeling, examining their potential benefits and critically analyzing the associated challenges. The study first shows the theoretical foundation of CVNNs and their relevance to modeling biological cognitive processes. Through detailed analysis of medical imaging case studies, the paper illustrates CVNNs superior capability in processing complex neural signals compared to traditional real-valued networks. However, this research also identifies major barriers, including theoretical interpretability issues, computational complexity, training instability, and an absence of a uniform evaluation standard. To address these challenges, the study proposes massive solutions that include interdisciplinary collaboration, specialized algorithm development, standardized benchmarking, and industrial application promotion. Overall, these recommendations form a strategic guide for translating CVNNs from theoretical constructs into practical, deployable technologies. The opinions derived from this work are particularly valuable for researchers and practitioners aiming to develop more biologically plausible and computationally efficient artificial intelligence systems.