基于Hopfield神经网络的观点动力学
Opinion Dynamics with Hopfield Neural Networks
摘要：
在最多10^8个节点的Hopfield神经网络中，我们通过Hebb耦合存储两个模式。然后，我们从第三个随机模式开始，它应该演变成两个存储模式之一，模拟联想记忆的认知过程，导致两种可能的意见之一。在概率p的情况下，每个神经元独立地取代了Hopfield规则，接管了另一个网络的相应值，从而模拟了不同的人如何说服对方。对于高p值，达成了共识。
英文摘要：
In Hopfield neural networks with up to 10^8 nodes we store two patterns through Hebb couplings. Then we start with a third random pattern which is supposed to evolve into one of the two stored patterns, simulating the cognitive process of associative memory leading to one of two possible opinions. With probability p each neuron independently, instead of following the Hopfield rule, takes over the corresponding value of another network, thus simulating how different people can convince each other. A consensus is achieved for high p.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
