Inferring structural connectivity using Ising couplings in models of neuronal networks
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2045-2322
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Inferring structural connectivity using Ising couplings in models of neuronal networks ; volume:7 ; number:1 ; day:15 ; month:8 ; year:2017 ; pages:1-12 ; date:12.2017
Scientific reports ; 7, Heft 1 (15.8.2017), 1-12, 12.2017
- Classification
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Biowissenschaften, Biologie
- Creator
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Kadirvelu, Balasundaram
- Contributor
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Hayashi, Yoshikatsu
Nasuto, Slawomir J.
SpringerLink (Online service)
- DOI
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10.1038/s41598-017-05462-2
- URN
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urn:nbn:de:1111-2018051716675
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:02 AM CEST
Data provider
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Associated
- Kadirvelu, Balasundaram
- Hayashi, Yoshikatsu
- Nasuto, Slawomir J.
- SpringerLink (Online service)
Other Objects (12)
Publisher Correction: Inferring structural connectivity using Ising couplings in models of neuronal networks
Inferring network connectivity using kinetic Ising models
Neuronal couplings inferred by an efficient inverse Ising method
Inferring neuronal functional connectivity using dynamic Bayesian networks
Inferring neural connectivity from multiple spike trains
Inferring causal connectivity from pairwise recordings and optogenetics
Inferring and quantifying causality in neuronal networks
Inferring functional connectivity in fMRI using minimum partial correlation
Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy
Inferring large-scale brain connectivity from spectral properties of the EEG
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity
Inferring the physical connectivity of complex networks from their functional dynamics
Publisher Correction: Inferring structural connectivity using Ising couplings in models of neuronal networks
Inferring network connectivity using kinetic Ising models
Neuronal couplings inferred by an efficient inverse Ising method
Inferring neuronal functional connectivity using dynamic Bayesian networks
Inferring neural connectivity from multiple spike trains
Inferring causal connectivity from pairwise recordings and optogenetics
Inferring and quantifying causality in neuronal networks
Inferring functional connectivity in fMRI using minimum partial correlation
Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy
Inferring large-scale brain connectivity from spectral properties of the EEG
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity
Inferring the physical connectivity of complex networks from their functional dynamics
Publisher Correction: Inferring structural connectivity using Ising couplings in models of neuronal networks
Inferring network connectivity using kinetic Ising models
Neuronal couplings inferred by an efficient inverse Ising method
Inferring neuronal functional connectivity using dynamic Bayesian networks
Inferring neural connectivity from multiple spike trains
Inferring causal connectivity from pairwise recordings and optogenetics
Inferring and quantifying causality in neuronal networks
Inferring functional connectivity in fMRI using minimum partial correlation
Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy
Inferring large-scale brain connectivity from spectral properties of the EEG
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity