Model reconstruction from temporal data for coupled oscillator networks

Panaggio, Mark J.; Ciocanel, Maria-Veronica; Lazarus, Lauren; Topaz, Chad M.; Xu, Bin

doi:10.1063/1.5120784

Cited by 24 publications

(11 citation statements)

References 46 publications

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“…An important result from our study is that the presence of dynamical noise can very greatly enhance the accuracy and applicability of our method (a similar point has been made in Ref. [14] and Ref. [15]), while observational noise degrades the ability to infer causal dependence.…”

Section: Short Term Causal Dependence (Stcd)supporting

confidence: 64%

“…Many past approaches have, for example, been based upon the concepts of prediction impact, [3,4] correlation, [7,8,9] information transfer, [10,11] and direct physical perturbations [12,13] . Other previous works have investigated the inference of network links from time series of node states assuming some prior knowledge of the form of the network system and using that knowledge in a fitting procedure to determine links [9,14,15,16,17] . In addition, some recent papers address network link inference from data via techniques based on delay coordinate embedding, [15] random forest methods, [18] network embedding algorithms [19] and feature ranking [20] .…”

Section: Introductionmentioning

confidence: 99%

“…We will show that this ML-based technique offers a unique and potentially highly effective approach to determining causal dependences. Furthermore, the presence of dynamical noise (either naturally present or intentionally injected) can very greatly improve the ability to infer causality, [14,15] while, in contrast, observational noise degrades inference.…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning to assess short term causal dependence and infer network links

Banerjee

Pathak

Roy

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We introduce and test a general machine-learning-based technique for the inference of short term causal dependence between state variables of an unknown dynamical system from time series measurements of its state variables. Our technique leverages the results of a machine learning process for short time prediction to achieve our goal. The basic idea is to use the machine learning to estimate the elements of the Jacobian matrix of the dynamical flow along an orbit. The type of machine learning that we employ is reservoir computing. We present numerical tests on link inference of a network of interacting dynamical nodes. It is seen that dynamical noise can greatly enhance the effectiveness of our technique, while observational noise degrades the effectiveness. We believe that the competition between these two opposing types of noise will be the key factor determining the success of causal inference in many of the most important application situations.1

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Section: Short Term Causal Dependence (Stcd)supporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using machine learning to assess short term causal dependence and infer network links

Banerjee

Pathak

Roy

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Longer signals such as those obtained in Resting State studies may demand considering the time evolution of the signal [14,15]. Our work focus on the influence of the different threshold values on the network not the temporal dynamics as shown in other approaches [16][17][18][19]. Since the coefficient r(x i , x j ) is symmetric, we consider undirected links: in a pair of correlated voxels there is not a source and a destination.…”

Section: Graph Implementationmentioning

confidence: 99%

Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks

et al. 2020

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Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were performed for all subjects. Besides this standard framework we have implemented a new approach consisting in filtering the data with respect to the fMRI paradigm (finger tapping) in order to obtain a better understanding of the network involved in the execution of the task. The main topological graph measures have been compared in both cases: voxel-voxel correlation and voxel-paradigm filtering plus voxel-voxel correlation. With the standard voxel-voxel correlation a clearly free-scale network was obtained. On the other hand, when we prefiltered the paradigm we obtained two different kind of networks: 1) free-scale; 2) random-like. To our best knowledge, this behaviour is reported here for first time for brain networks. We suggest that paradigm signal prefiltering can provide more infomation about the brain networks.

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“…Less demanding passive methods have been devised, which rely only on observations of the agents dynamics. Some are based on the optimization of a cost function, and require a computation time that scales at least as O(n 4 ), with the number n of agents [21][22][23].…”

mentioning

confidence: 99%

Reconstructing Network Structures from Partial Measurements

Tyloo

Delabays

Jacquod

2021

Preprint

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The dynamics of systems of interacting agents is determined by the structure of their coupling network. The knowledge of the latter is therefore highly desirable, for instance to develop efficient control schemes, to accurately predict the dynamics or to better understand inter-agent processes. In many important and interesting situations, the network structure is not known, however, and previous investigations have shown how it may be inferred from complete measurement time series, on each and every agent. These methods implicitly presuppose that, even though the network is not known, all its nodes are. A major shortcoming of theirs is that they cannot provide any reliable information, not even on partial network structures, as soon as some agents are unobservable. Here, we construct a novel method that determines network structures even when not all agents are measurable. We establish analytically and illustrate numerically that velocity signal correlators encode not only direct couplings, but also geodesic distances in the coupling network, within the subset of measurable agents. When dynamical data are accessible for all agents, our method is furthermore algorithmically more efficient than the traditional ones, because it does not rely on matrix inversion.

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Model reconstruction from temporal data for coupled oscillator networks

Cited by 24 publications

References 46 publications

Using machine learning to assess short term causal dependence and infer network links

Using machine learning to assess short term causal dependence and infer network links

Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks

Reconstructing Network Structures from Partial Measurements

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