Khalid Mustafa, Wu Jun, M Ali Taghreed, Ameen Thaair, Moustafa Ahmed A, Zhu Qiuguo, Xiong Rong
The State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China.
The Binhai Industrial Technology Research Institute of Zhejiang University, Tianjin 300301, China.
Brain Sci. 2020 Jul 7;10(7):431. doi: 10.3390/brainsci10070431.
Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow-Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.
大多数现有的皮质-海马计算模型使用不同的人工神经网络拓扑结构。这些传统方法模拟各种生物学范式，由于两个原因可能会导致训练缓慢和条件反应不足：条件刺激数量的增加以及不同阶段模拟生物学范式的复杂性增加。本文提出了一种用于完整和受损系统建模的皮质-海马计算量子（CHCQ）模型。CHCQ模型是第一个使用量子神经网络来模拟生物学范式的计算模型。该模型由两个纠缠的量子神经网络组成：一个自适应单层前馈量子神经网络和一个自编码器量子神经网络。CHCQ模型使用量子内星、外星和威德罗-霍夫学习算法自适应地更新其量子神经网络的所有权重。我们的模型成功模拟了多个生物学过程，并快速有效地维持了输出条件反应。此外，结果与先前的生物学研究一致。
