Cortico-Hippocampal Computational Modeling Using Quantum-Inspired Neural Networks - Khalid, Mustafa; Wu, Jun; Ali, Taghreed M.; Ameen, Thaair; Altaher, Ali Salem; Moustafa, Ahmed A.; Zhu, Qiuguo; Xiong, Rong (Frontiers in Computational Neuroscience, 2020)
Many of the current computational models that mimic cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are substantially slow, occasionally taking thousands of trials to obtain the final response with an implausible error. The long time and inaccurate output response are caused by the complexity of the input cue and simulated biological processes. An intact and lesioned system of a cortico-hippocampal computational model using quantum-inspired neural networks (CHCQI) is proposed in this study. The CHCQI model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model simulated various classical conditioned tasks that are related to different biological processes. The output responses of the simulated tasks obtained the desired output quickly and efficiently compared with those of other computational models, including the recently published Green model.