Computing With Networks of Chemical Oscillators and its Application for Schizophrenia Diagnosis

Abstract

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.

Information Processing Using Networks of Chemical Oscillators

“…In a number of recent papers, it has been shown that networks of interacting chemical oscillators can be trained to perform classification tasks with reasonable accuracy [45,46,49]. Let us consider a problem A defined by a set of records D A = {r n , n = 1, N}.…”

“…Coupling between oscillators, indicated by arrows in Figure 3, is achieved by reactions that extend the original Oregonator model. I assume that the coupling is of the activatory type and occurs via the exchange of reactor activators between oscillators [49]. Let U i denote the activator of the ith node.…”

The Concilium of Information Processing Networks of Chemical Oscillators for Determining Drug Response in Patients With Multiple Myeloma

“…The coupling between oscillators, indicated by two direction arrows in Figure 4A is achieved by reactions that extend the original Oregonator model. We assume that the coupling is of the activatory type and occurs via the exchange of reactor activators between oscillators (Gorecki and Bose, 2020;Bose and Gorecki, 2022). Let U i denotes the activator of the ith oscillator.…”

Model of an Artificial Blastula for Assessing Development Toxicity

“…Evolutionary algorithms are frequently used for parameter optimization. Once optimized, even a small network can classify schizophrenia [40] or color of points on the Japanese flag [41] with accuracy exceeding 90%.…”