Chaos: An Interdisciplinary Journal of Nonlinear Science最新文献_第2页

Impact of coupling on neuronal extreme events: Mitigation and enhancement 耦合对神经元极端事件的影响:缓解和增强

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0158135

A. Roy, S. Sinha

{"title":"Impact of coupling on neuronal extreme events: Mitigation and enhancement","authors":"A. Roy, S. Sinha","doi":"10.1063/5.0158135","DOIUrl":"https://doi.org/10.1063/5.0158135","url":null,"abstract":"We focus on the emergence of extreme events in a collection of aperiodic neuronal maps, under local diffusive coupling, as well as global mean-field coupling. Our central finding is that local diffusive coupling enhances the probability of occurrence of both temporal and spatial extreme events, while in marked contrast, global mean-field coupling suppresses extreme events. So the nature of the coupling crucially determines whether the extreme events are enhanced or mitigated by coupling. Further, in globally coupled systems, there exist initial states in a window of coupling strength that exhibit spatial extreme events, but not temporal extreme events, suggesting that spatial extreme events do not imply temporal extreme events. We also explored the existence of discernible patterns in the return maps of successive inter-event intervals in order to gauge short-term risk-assessment. We find that single neuronal maps, as well as systems under strong diffusive coupling, display broad noisy patterns in these return maps, with clusters around characteristic intervals, allowing some short-term predictability in the extreme event sequence. In contrast, under weak diffusive coupling and global coupling, inter-event intervals lose all perceptible correlations, and the distribution extends to very large inter-event intervals. Lastly, we investigated a non-local diffusive coupling form. Interestingly, this coupling yielded a large window where temporal extreme events occurred, but the spatial profile was synchronized, namely, we found synchronized temporal extreme events. Such synchronized extreme spiking is reminiscent of the neuronal activity leading to epileptic seizures and is of potential relevance to extreme events in brain activity.","PeriodicalId":340975,"journal":{"name":"Chaos: An Interdisciplinary Journal of Nonlinear Science","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123599365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bottom-up approach for recurrence detection based on sampling distance 基于采样距离的自底向上的重复检测方法

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0160832

R. Delage, T. Nakata

{"title":"A bottom-up approach for recurrence detection based on sampling distance","authors":"R. Delage, T. Nakata","doi":"10.1063/5.0160832","DOIUrl":"https://doi.org/10.1063/5.0160832","url":null,"abstract":"One of the major problems faced in the recurrence analysis of dynamical systems is the tangential motion effect affecting the structures in recurrence plots and their quantification. This issue roots to the choice of a threshold for recurrence, making it a crucial parameter for such analyses. It has been shown that a variable threshold following the dynamical changes of the system is more suited to the analysis of non-stationary data as it mitigates this effect. We study here the use of the distance separating successive points in the phase space as a reference for the recurrence threshold. The method relies on a single parameter while qualitatively and quantitatively providing stable recurrence structures as the previously suggested threshold based on the local maximum pairwise distance. This complete bottom-up approach is shown to be beneficial in the presence of abrupt transitions. It is also fairly noise-resistant and is not dependent on the sampling frequency in its normalized formulation. Furthermore, the sampling distance provides a clear reference for the occurrence of the tangential motion effect, allowing to define a default value for the threshold parameter to avoid it.","PeriodicalId":340975,"journal":{"name":"Chaos: An Interdisciplinary Journal of Nonlinear Science","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Li–Yorke chaos of linear differential equations in a finite-dimensional space with a weak topology 有限维弱拓扑空间中线性微分方程的Li-Yorke混沌

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0163463

Xu Zhang, Nan Jiang, Qigui Yang, Guanrong Chen

引用次数: 0

A multiplier-free Rulkov neuron under memristive electromagnetic induction: Dynamics analysis, energy calculation, and circuit implementation 忆阻电磁感应下无乘法器的Rulkov神经元:动力学分析、能量计算和电路实现

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0160751

Shaohua Zhang, Cong Wang, Hongli Zhang, Hairong Lin

{"title":"A multiplier-free Rulkov neuron under memristive electromagnetic induction: Dynamics analysis, energy calculation, and circuit implementation","authors":"Shaohua Zhang, Cong Wang, Hongli Zhang, Hairong Lin","doi":"10.1063/5.0160751","DOIUrl":"https://doi.org/10.1063/5.0160751","url":null,"abstract":"Establishing a realistic and multiplier-free implemented biological neuron model is significant for recognizing and understanding natural firing behaviors, as well as advancing the integration of neuromorphic circuits. Importantly, memristors play a crucial role in constructing memristive neuron and network models by simulating synapses or electromagnetic induction. However, existing models lack the consideration of initial-boosted extreme multistability and its associated energy analysis. To this end, we propose a multiplier-free implementation of the Rulkov neuron model and utilize a periodic memristor to represent the electromagnetic induction effect, thereby achieving the biomimetic modeling of the non-autonomous memristive Rulkov (mRulkov) neuron. First, theoretical analysis demonstrates that the stability distribution of the time-varying line equilibrium point is determined by both the parameters and the memristor’s initial condition. Furthermore, numerical simulations show that the mRulkov neuron can exhibit parameter-dependent local spiking, local hidden spiking, and periodic bursting firing behaviors. In addition, based on the periodic characteristics of the memductance function, the topological invariance of the mRulkov neuron is comprehensively proved. Therefore, local basins of attraction, bifurcation diagrams, and attractors related to extreme multistability can be boosted by switching the memristor’s initial condition. Significantly, the novel boosted extreme multistability is discovered in the Rulkov neuron for the first time. More importantly, the energy transition associated with the boosting dynamics is revealed through computing the Hamilton energy distribution. Finally, we develop a simulation circuit for the non-autonomous mRulkov neuron and confirm the effectiveness of the multiplier-free implementation and the accuracy of the numerical results through PSpice simulations.","PeriodicalId":340975,"journal":{"name":"Chaos: An Interdisciplinary Journal of Nonlinear Science","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121517786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symbolic regression via neural networks 通过神经网络进行符号回归

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0134464

N. Boddupalli, T. Matchen, J. Moehlis

引用次数: 1

Sign patterns symbolization and its use in improved dependence test for complex network inference 符号模式符号化及其在复杂网络推理改进依赖检验中的应用

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0160868

Arthur Matsuo Yamashita Rios de Sousa, J. Hlinka

{"title":"Sign patterns symbolization and its use in improved dependence test for complex network inference","authors":"Arthur Matsuo Yamashita Rios de Sousa, J. Hlinka","doi":"10.1063/5.0160868","DOIUrl":"https://doi.org/10.1063/5.0160868","url":null,"abstract":"Inferring the dependence structure of complex networks from the observation of the non-linear dynamics of its components is among the common, yet far from resolved challenges faced when studying real-world complex systems. While a range of methods using the ordinal patterns framework has been proposed to particularly tackle the problem of dependence inference in the presence of non-linearity, they come with important restrictions in the scope of their application. Hereby, we introduce the sign patterns as an extension of the ordinal patterns, arising from a more flexible symbolization which is able to encode longer sequences with lower number of symbols. After transforming time series into sequences of sign patterns, we derive improved estimates for statistical quantities by considering necessary constraints on the probabilities of occurrence of combinations of symbols in a symbolic process with prohibited transitions. We utilize these to design an asymptotic chi-squared test to evaluate dependence between two time series and then apply it to the construction of climate networks, illustrating that the developed method can capture both linear and non-linear dependences, while avoiding bias present in the naive application of the often used Pearson correlation coefficient or mutual information.","PeriodicalId":340975,"journal":{"name":"Chaos: An Interdisciplinary Journal of Nonlinear Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116913452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing spiking neural networks for robust and reconfigurable computation 设计具有鲁棒性和可重构计算的峰值神经网络

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0156447

Georg Börner, Fabio Schittler Neves, M. Timme

{"title":"Designing spiking neural networks for robust and reconfigurable computation","authors":"Georg Börner, Fabio Schittler Neves, M. Timme","doi":"10.1063/5.0156447","DOIUrl":"https://doi.org/10.1063/5.0156447","url":null,"abstract":"Networks of spiking neurons constitute analog systems capable of effective and resilient computing. Recent work has shown that networks of symmetrically connected inhibitory neurons may implement basic computations such that they are resilient to system disruption. For instance, if the functionality of one neuron is lost (e.g., the neuron, along with its connections, is removed), the system may be robustly reconfigured by adapting only one global system parameter. How to effectively adapt network parameters to robustly perform a given computation is still unclear. Here, we present an analytical approach to derive such parameters. Specifically, we analyze k-winners-takes-all (k-WTA) computations, basic computational tasks of identifying the k largest signals from a total of N input signals from which one can construct any computation. We identify and characterize different dynamical regimes and provide analytical expressions for the transitions between different numbers k of winners as a function of both input and network parameters. Our results thereby provide analytical insights about the dynamics underlying k-winner-takes-all functionality as well as an effective way of designing spiking neural network computing systems implementing disruption-resilient dynamics.","PeriodicalId":340975,"journal":{"name":"Chaos: An Interdisciplinary Journal of Nonlinear Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117056640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior 生物圈-大气界面高频湍流中的因果相互作用:结构行为

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0131468

L. C. Hernandez Rodriguez, Praveen Kumar

{"title":"Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior","authors":"L. C. Hernandez Rodriguez, Praveen Kumar","doi":"10.1063/5.0131468","DOIUrl":"https://doi.org/10.1063/5.0131468","url":null,"abstract":"High-frequency (e.g., 10 Hz) eddy covariance measurements are typically used to estimate fluxes at the land–atmosphere interface at timescales of 15–60 min. These multivariate data contain information about the interdependency at high frequency between the interacting variables such as wind, humidity, temperature, and CO2. We use data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US, which offers an opportunity to move beyond the traditional spectral analyses to explore causal dependency among variables. In this study, we quantify the structure of inter-dependencies of interacting variables at high frequency represented by a directed acyclic graph (DAG). We compare DAGs to investigate changes in structural differences in causal interactions. We then apply a distance-based classification and k-means clustering approach to identify the evolution of the causal structure represented by a DAG. Our method selects an unbiased number of clusters of similar structures and characterizes the similarities and differences between them. We explore a range of dynamic behavior using data from a clear sky day and during a solar eclipse in 2017. Our results show well-defined clusters of similar causal dependencies as the system evolves. Our approach provides a methodological framework to understand how causal dependence in turbulence manifests in high-frequency data when represented through a DAG.","PeriodicalId":340975,"journal":{"name":"Chaos: An Interdisciplinary Journal of Nonlinear Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122391235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi-scroll attractor and its broken coexisting attractors in cyclic memristive neural network 循环记忆神经网络中的多涡旋吸引子及其破碎共存吸引子

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0159391

Q. Lai, Yidan Chen

引用次数: 0

Chimeras in phase oscillator networks locally coupled through an auxiliary field: Stability and bifurcations 辅助场局部耦合相振网络中的嵌合体:稳定性和分岔

Chaos: An Interdisciplinary Journal of Nonlinear Science Pub Date : 2023-08-01 DOI: 10.1063/5.0156627

C. Laing

引用次数: 0