Bin Liu, Muning Li, Zhijun Li, Yaonan Tong, Zhaoyu Li, Chunlai Li
{"title":"Firing dynamics and coupling synchronization of memristive EMR-based Chaivlo neuron utilizing equivalent energy approach.","authors":"Bin Liu, Muning Li, Zhijun Li, Yaonan Tong, Zhaoyu Li, Chunlai Li","doi":"10.1063/5.0229072","DOIUrl":null,"url":null,"abstract":"<p><p>Firing dynamics and its energy property of neuron are crucial for exploring the mechanism of intricate information processing within the nervous system. However, the energy analysis of discrete neuron is significantly lacking in comparison to the vast literature and mature theory available on continuous neuron, thereby necessitating a focused effort in this underexplored realm. In this paper, we introduce a Chaivlo neuron map by employing a flux-controlled memristor to simulate electromagnetic radiation (EMR), and a detailed analysis of its firing dynamics is conducted based on an equivalent Hamiltonian energy approach. Our observations reveal that a range of energy-based firing behaviors, such as spike firing, coexistence firing, mixed-mode firing, and chaotic bursting firing, can be induced by EMR and injected current. To delve deeper into the synchronous firing dynamics, we establish a Chaivlo network by electrically coupling two memristive EMR-based Chaivlo neurons. Subsequently, we experimentally evaluate the synchronization behavior of this network by quantifying both the synchronization factor and the average difference of equivalent Hamiltonian energy. Our findings conclusively demonstrate that both EMR and coupling strength positively contribute to the network's synchronization ability.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1063/5.0229072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Firing dynamics and its energy property of neuron are crucial for exploring the mechanism of intricate information processing within the nervous system. However, the energy analysis of discrete neuron is significantly lacking in comparison to the vast literature and mature theory available on continuous neuron, thereby necessitating a focused effort in this underexplored realm. In this paper, we introduce a Chaivlo neuron map by employing a flux-controlled memristor to simulate electromagnetic radiation (EMR), and a detailed analysis of its firing dynamics is conducted based on an equivalent Hamiltonian energy approach. Our observations reveal that a range of energy-based firing behaviors, such as spike firing, coexistence firing, mixed-mode firing, and chaotic bursting firing, can be induced by EMR and injected current. To delve deeper into the synchronous firing dynamics, we establish a Chaivlo network by electrically coupling two memristive EMR-based Chaivlo neurons. Subsequently, we experimentally evaluate the synchronization behavior of this network by quantifying both the synchronization factor and the average difference of equivalent Hamiltonian energy. Our findings conclusively demonstrate that both EMR and coupling strength positively contribute to the network's synchronization ability.