Role of assortativity in predicting burst synchronization using echo state network

Mousumi Roy, Abhishek Senapati, Swarup Poria, Arindam Mishra, Chittaranjan Hens

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7 Scopus citations


In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in the machine's prediction. However, in the case of assortative network, training the machine with the information (i.e., time series) of low degree nodes gives better results in predicting the burst synchronization. The results are found to be consistent with the investigation carried out with a continuous time Hindmarsh-Rose neuron model. Furthermore, the role of hyperparameters like spectral radius and leaking parameter of ESN on the prediction process has been examined. Finally, we explain the underlying mechanism responsible for observing these differences in the prediction in a degree correlated network.

Original languageEnglish
Article number064205
JournalPhysical Review E
Issue number6
StatePublished - Jun 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 American Physical Society.


M.R. is financially supported by University Grant Commission, Government of India. A.S. is funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany's Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. A.M. has been supported by the National Science Centre, Poland, OPUS Programme Project No. 2018/29/B/ST8/00457. C.H. is supported by DST-INSPIRE-Faculty grant (Grant No. IFA17-PH193).

FundersFunder number
Center of Advanced Systems Understanding
Saxon Ministry for Science, Culture and Tourism
University Grants Commission
Bundesministerium für Bildung und Forschung
Narodowe Centrum Nauki2018/29/B/ST8/00457
Sächsisches Staatsministerium für Wissenschaft und Kunst


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