Bose Ashmita, Gorecki Jerzy
Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland.
Front Chem. 2022 Feb 16;10:848685. doi: 10.3389/fchem.2022.848685. eCollection 2022.
Chemical reactions are responsible for information processing in living organisms, yet biomimetic computers are still at the early stage of development. The bottom-up design strategy commonly used to construct semiconductor information processing devices is not efficient for chemical computers because the lifetime of chemical logic gates is usually limited to hours. It has been demonstrated that chemical media can efficiently perform a specific function like labyrinth search or image processing if the medium operates in parallel. However, the number of parallel algorithms for chemical computers is very limited. Here we discuss top-down design of such algorithms for a network of chemical oscillators that are coupled by the exchange of reaction activators. The output information is extracted from the number of excitations observed on a selected oscillator. In our model of a computing network, we assume that there is an external factor that can suppress oscillations. This factor can be applied to control the nodes and introduce input information for processing by a network. We consider the relationship between the number of oscillation nodes and the network accuracy. Our analysis is based on computer simulations for a network of oscillators described by the Oregonator model of a chemical oscillator. As the example problem that can be solved with an oscillator network, we consider schizophrenia diagnosis on the basis of EEG signals recorded using electrodes located at the patient's scalp. We demonstrated that a network formed of interacting chemical oscillators can process recorded signals and help to diagnose a patient. The parameters of considered networks were optimized using an evolutionary algorithm to achieve the best results on a small training dataset of EEG signals recorded from 45 ill and 39 healthy patients. For the optimized networks, we obtained over 82% accuracy of schizophrenia detection on the training dataset. The diagnostic accuracy can be increased to almost 87% if the majority rule is applied to answers of three networks with different number of nodes.
化学反应负责生物体中的信息处理，但仿生计算机仍处于发展的早期阶段。用于构建半导体信息处理设备的自下而上设计策略对化学计算机并不高效，因为化学逻辑门的寿命通常限于数小时。已经证明，如果化学介质并行运行，它可以有效地执行特定功能，如迷宫搜索或图像处理。然而，化学计算机的并行算法数量非常有限。在这里，我们讨论了一种自上而下的算法设计，该算法用于由反应激活剂交换耦合的化学振荡器网络。输出信息从选定振荡器上观察到的激发次数中提取。在我们的计算网络模型中，我们假设存在一个可以抑制振荡的外部因素。这个因素可用于控制节点并引入输入信息以供网络处理。我们考虑振荡节点数量与网络精度之间的关系。我们的分析基于对由化学振荡器的俄勒冈模型描述的振荡器网络的计算机模拟。作为可以用振荡器网络解决的示例问题，我们考虑基于使用位于患者头皮上的电极记录的脑电图信号进行精神分裂症诊断。我们证明，由相互作用的化学振荡器组成的网络可以处理记录的信号并有助于诊断患者。使用进化算法对所考虑网络的参数进行了优化，以在从45名患病和39名健康患者记录的脑电图信号的小训练数据集上获得最佳结果。对于优化后的网络，我们在训练数据集上获得了超过82%的精神分裂症检测准确率。如果将多数规则应用于具有不同节点数量的三个网络的答案，诊断准确率可提高到近87%。
