Event detection, multimodality and non-stationarity: Ordinal patterns, a tool to rule them all?

Arroyo, David; Chamorro, Pablo Palenzuela; Amigó, José M.; Rodríguez, Francisco B.; Varona, Pablo

doi:10.1140/epjst/e2013-01852-9

Cited by 10 publications

(6 citation statements)

References 57 publications

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“…SCCs are a standard tool to analyze spike trains [33,34], however, they only capture linear correlations. In contrast, a symbolic methodology known as ordinal analysis [22] has been demonstrated to be well suited for detecting nonlinear correlations in spike trains [13,19,35]. In this approach the actual ISI values {I 1 , ..., I i , ..., I N } are not taken into account, instead, their relative temporal ordering is considered.…”

Section: Methodsmentioning

confidence: 99%

“…For example, neuronal populations can encode information in the spike rate, in the spike timing, in the frequency content of spike sequences, in the coherence of spatial spike patterns, etc. Linear and non-linear data-driven methods have been developed to quantify the information content of neuronal activity [10][11][12][13]. A lot of research has focused on the statistics of the time intervals between consecutive spikes (inter-spike intervals, ISIs) and how properties such as ISI correlations affect information encoding [14][15][16][17][18].Recently, the response of an individual neuron to a weak periodic signal was studied numerically [19], in the framework of the FitzHugh-Nagumo model [20,21].…”

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confidence: 99%

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Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Masoliver

Masoller

2018

Sci Rep

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Despite intensive research, the mechanisms underlying the neural code remain poorly understood. Recent work has focused on the response of a single neuron to a weak, sub-threshold periodic signal. By simulating the stochastic FitzHugh-Nagumo (FHN) model and then using a symbolic method to analyze the firing activity, preferred and infrequent spike patterns (defined by the relative timing of the spikes) were detected, whose probabilities encode information about the signal. As not individual neurons but neuronal populations are responsible for sensory coding and information transfer, a relevant question is how a second neuron, which does not perceive the signal, affects the detection and the encoding of the signal, done by the first neuron. Through simulations of two stochastic FHN neurons we show that the encoding of a sub-threshold signal in symbolic spike patterns is a plausible mechanism. The neuron that perceives the signal fires a spike train that, despite having an almost random temporal structure, has preferred and infrequent patterns which carry information about the signal. Our findings could be relevant for sensory systems composed by two noisy neurons, when only one detects a weak external input.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Masoliver

Masoller

2018

Sci Rep

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“…Thus, the time scaling algorithm performs a time calibration by measuring the number of points needed to accurately represent reference events in the dynamics of the living neuron, taking into account the model precise integration and the chosen acquisition sampling rate. Reference events that the user can define are, for example, action potentials or bursts (Arroyo et al, 2013;Varona et al, 2016). The sampling rate determines the required discretization of the signals from the living neuron and the model.…”

Section: Time Scalingmentioning

confidence: 99%

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

et al. 2020

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Hybrid circuits built by creating mono-or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, drift compensation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates building hybrid circuits and closedloop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

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“…It is known that the topological 37 and metric 38 entropies obtained by permutations agree with the conventional topological and metric entropies, respectively, when the length of permutations is sufficiently large. The joint permutations 39,40 and joint metric permutation entropy 39,41,42 were considered previously, but the joint topological permutation entropy is used for the first time in this paper as far as we noticed.…”

Section: -4mentioning

confidence: 99%

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

Hirata

Shiro

Takahashi

et al. 2015

Chaos

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A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis Chaos: An Interdisciplinary Journal of Nonlinear Science 26, 063109 (2016) The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data. Modeling and predicting a high-dimensional time series is still a challenge because common methods such as neural networks 1-5 and radial basis functions 6-11 have many parameters to be fitted compared with the length of the time series. This challenge is often called as the curse of dimensionality.12 Here, we propose to model a high-dimensional time series with barycentric coordinates 13 by using linear programming. 14 Mees 13 demonstrated that barycentric coordinates obtained by tessellations are effective to reproduce typical behavior of the two-dimensional H enon map and the threedimensional R€ ossler model, while the tessellations are difficult to be applied to high-dimensional phase space. We overcome this difficulty by formulating the problem for obtaining barycentric coordinates, employing linear programming and expressing the approximation error directly. Toy and real-world examples show the wide applicability of the proposed method.

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Event detection, multimodality and non-stationarity: Ordinal patterns, a tool to rule them all?

Cited by 10 publications

References 57 publications

Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

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