Inferring global exponents in subsampled neural systems
In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents ma...
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| Published in | iScience Vol. 28; no. 8; p. 113049 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
15.08.2025
Elsevier |
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| Online Access | Get full text |
| ISSN | 2589-0042 2589-0042 |
| DOI | 10.1016/j.isci.2025.113049 |
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| Abstract | In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. In this study, using branching process and (2 + 1)D directed percolation, we show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis (DFA) of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. This robustness derives from the preservation of long-time correlations in the subsampled signal, even though large avalanches can be fragmented into smaller ones. These results don’t depend on the specific model and may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system.
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•Neural activity exhibits avalanche dynamics with power-law size and duration distributions•Subsampling alters these distributions and biases estimates of critical exponents•Exponents governing power spectrum and DFA remain stable under subsampling•This robustness enables inferring global critical exponents reliably from partial data
Natural sciences; Biological sciences; Neural networks |
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| AbstractList | In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. In this study, using branching process and (2 + 1)D directed percolation, we show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis (DFA) of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. This robustness derives from the preservation of long-time correlations in the subsampled signal, even though large avalanches can be fragmented into smaller ones. These results don’t depend on the specific model and may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system.
[Display omitted]
•Neural activity exhibits avalanche dynamics with power-law size and duration distributions•Subsampling alters these distributions and biases estimates of critical exponents•Exponents governing power spectrum and DFA remain stable under subsampling•This robustness enables inferring global critical exponents reliably from partial data
Natural sciences; Biological sciences; Neural networks In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. In this study, using branching process and (2 + 1)D directed percolation, we show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis (DFA) of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. This robustness derives from the preservation of long-time correlations in the subsampled signal, even though large avalanches can be fragmented into smaller ones. These results don’t depend on the specific model and may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system. In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. In this study, using branching process and (2 + 1)D directed percolation, we show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis (DFA) of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. This robustness derives from the preservation of long-time correlations in the subsampled signal, even though large avalanches can be fragmented into smaller ones. These results don't depend on the specific model and may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system.In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. In this study, using branching process and (2 + 1)D directed percolation, we show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis (DFA) of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. This robustness derives from the preservation of long-time correlations in the subsampled signal, even though large avalanches can be fragmented into smaller ones. These results don't depend on the specific model and may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system. In systems exhibiting avalanche-like activity, critical exponents can provide insights into the mechanisms underlying the observed behavior or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. In this study, using branching process and (2 + 1)D directed percolation, we show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis (DFA) of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. This robustness derives from the preservation of long-time correlations in the subsampled signal, even though large avalanches can be fragmented into smaller ones. These results don’t depend on the specific model and may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system. • Neural activity exhibits avalanche dynamics with power-law size and duration distributions • Subsampling alters these distributions and biases estimates of critical exponents • Exponents governing power spectrum and DFA remain stable under subsampling • This robustness enables inferring global critical exponents reliably from partial data Natural sciences; Biological sciences; Neural networks |
| ArticleNumber | 113049 |
| Author | Conte, Davide de Candia, Antonio |
| Author_xml | – sequence: 1 givenname: Davide surname: Conte fullname: Conte, Davide email: davide.conte@unicampania.it organization: Department of Mathematics & Physics, University of Campania “Luigi Vanvitelli”, viale Lincoln 5, 81100 Caserta, Italy – sequence: 2 givenname: Antonio surname: de Candia fullname: de Candia, Antonio email: antonio.decandia@unina.it organization: Dipartimento di Fisica “E. Pancini”, Università di Napoli Federico II, Complesso Universitario di Monte Sant’Angelo, via Cintia, 80126 Napoli, Italy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40727941$$D View this record in MEDLINE/PubMed |
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| Keywords | Natural sciences biological sciences neural networks |
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