Mechanisms of spikes generation in piecewise Rulkov model

PHYSICS, TECHNOLOGIES AND INNOVATION (PTI-2019): Proceedings of the VI International Young Researchers’ Conference Pub Date : 2019-12-06 DOI:10.1063/1.5134235

A. Belyaev, T. Ryazanova

{"title":"Mechanisms of spikes generation in piecewise Rulkov model","authors":"A. Belyaev, T. Ryazanova","doi":"10.1063/1.5134235","DOIUrl":null,"url":null,"abstract":"In this paper a piecewise discontinuous Rulkov neuron model is considered. It is shown that even in the case of a one-dimensional map, the presence of a random perturbation leads to the spiking. Two mechanisms of spike generation caused by the random behavior of one of the parameters are investigated. We show that the coexistence of two attractors is not the only prerequisite for the occurrence of spiking. The confidence domain method based on stochastic sensitivity function is successfully applied to predict the level of the noise intensity necessary to the spike generation. The main statistical characteristics of interspike intervals depending on the noise intensity are demonstrated.In this paper a piecewise discontinuous Rulkov neuron model is considered. It is shown that even in the case of a one-dimensional map, the presence of a random perturbation leads to the spiking. Two mechanisms of spike generation caused by the random behavior of one of the parameters are investigated. We show that the coexistence of two attractors is not the only prerequisite for the occurrence of spiking. The confidence domain method based on stochastic sensitivity function is successfully applied to predict the level of the noise intensity necessary to the spike generation. The main statistical characteristics of interspike intervals depending on the noise intensity are demonstrated.","PeriodicalId":418936,"journal":{"name":"PHYSICS, TECHNOLOGIES AND INNOVATION (PTI-2019): Proceedings of the VI International Young Researchers’ Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PHYSICS, TECHNOLOGIES AND INNOVATION (PTI-2019): Proceedings of the VI International Young Researchers’ Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/1.5134235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

Abstract

In this paper a piecewise discontinuous Rulkov neuron model is considered. It is shown that even in the case of a one-dimensional map, the presence of a random perturbation leads to the spiking. Two mechanisms of spike generation caused by the random behavior of one of the parameters are investigated. We show that the coexistence of two attractors is not the only prerequisite for the occurrence of spiking. The confidence domain method based on stochastic sensitivity function is successfully applied to predict the level of the noise intensity necessary to the spike generation. The main statistical characteristics of interspike intervals depending on the noise intensity are demonstrated.In this paper a piecewise discontinuous Rulkov neuron model is considered. It is shown that even in the case of a one-dimensional map, the presence of a random perturbation leads to the spiking. Two mechanisms of spike generation caused by the random behavior of one of the parameters are investigated. We show that the coexistence of two attractors is not the only prerequisite for the occurrence of spiking. The confidence domain method based on stochastic sensitivity function is successfully applied to predict the level of the noise intensity necessary to the spike generation. The main statistical characteristics of interspike intervals depending on the noise intensity are demonstrated.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

分段Rulkov模型中尖峰产生的机理

本文考虑了一个分段不连续的Rulkov神经元模型。结果表明，即使在一维映射的情况下，随机扰动的存在也会导致尖峰。研究了由其中一个参数的随机行为引起的脉冲产生的两种机制。我们证明了两个吸引子的共存并不是发生尖峰的唯一先决条件。应用基于随机灵敏度函数的置信域方法成功地预测了峰值产生所需的噪声强度水平。证明了依赖于噪声强度的峰间间隔的主要统计特性。本文考虑了一个分段不连续的Rulkov神经元模型。结果表明，即使在一维映射的情况下，随机扰动的存在也会导致尖峰。研究了由其中一个参数的随机行为引起的脉冲产生的两种机制。我们证明了两个吸引子的共存并不是发生尖峰的唯一先决条件。应用基于随机灵敏度函数的置信域方法成功地预测了峰值产生所需的噪声强度水平。证明了依赖于噪声强度的峰间间隔的主要统计特性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

PHYSICS, TECHNOLOGIES AND INNOVATION (PTI-2019): Proceedings of the VI International Young Researchers’ Conference

自引率

0.00%

发文量

期刊最新文献

Probabilistic model of error distribution for satellite navigation Measurement of the directional dose equivalent at workplaces of a nuclear power plant Morphological stability of the interface of a bubble growing in a fluid. Two-dimensional case Red-ox processes involving niobium in alkali chloride melts Movement of head and center of mass: Joint assessment