Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.

中文翻译：

---

使用量子启发神经网络的皮质-海马计算建模

许多当前旨在模拟大脑皮层和海马模块的计算模型依赖于人工神经网络。然而，这种经典甚至是深度神经网络非常缓慢，有时需要进行数千次试验才能获得具有相当大误差的最终响应。需要大量的学习试验和不准确的输出响应是由于输入线索和被模拟的生物过程的复杂性。本文提出了一个使用量子启发神经网络的完整和受损皮质-海马系统的计算模型。这种皮质-海马计算量子启发 (CHCQI) 模型通过使用与量子电路纠缠的自适应更新神经网络来模拟皮质和海马模块。所提出的模型用于模拟与生物过程相关的各种经典调节任务。与其他计算模型（包括最近发布的 Green 模型）相比，模拟任务的输出可以快速有效地产生所需的响应。