Publisher Correction: Inferring structural connectivity using Ising couplings in models of neuronal networks
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2045-2322
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Publisher Correction: Inferring structural connectivity using Ising couplings in models of neuronal networks ; volume:8 ; number:1 ; day:14 ; month:3 ; year:2018 ; pages:1-4 ; date:12.2018
Scientific reports ; 8, Heft 1 (14.3.2018), 1-4, 12.2018
- Classification
-
Biowissenschaften, Biologie
- Creator
-
Kadirvelu, Balasundaram
- Contributor
-
Hayashi, Yoshikatsu
Nasuto, Slawomir J.
SpringerLink (Online service)
- DOI
-
10.1038/s41598-018-22950-1
- URN
-
urn:nbn:de:1111-2018051617414
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:59 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kadirvelu, Balasundaram
- Hayashi, Yoshikatsu
- Nasuto, Slawomir J.
- SpringerLink (Online service)
Other Objects (12)
Inferring structural connectivity using Ising couplings in models of neuronal networks
Inferring network connectivity using kinetic Ising models
Inferring neural connectivity from multiple spike trains
Neuronal couplings inferred by an efficient inverse Ising method
Inferring neuronal functional connectivity using dynamic Bayesian networks
Inferring causal connectivity from pairwise recordings and optogenetics
Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy
Inferring functional connectivity in fMRI using minimum partial correlation
Inferring large-scale brain connectivity from spectral properties of the EEG
Inferring the physical connectivity of complex networks from their functional dynamics
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity
Inferring functional brain connectivity from field-potential oscillations in health and disease
Inferring structural connectivity using Ising couplings in models of neuronal networks
Inferring network connectivity using kinetic Ising models
Inferring neural connectivity from multiple spike trains
Neuronal couplings inferred by an efficient inverse Ising method
Inferring neuronal functional connectivity using dynamic Bayesian networks
Inferring causal connectivity from pairwise recordings and optogenetics
Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy
Inferring functional connectivity in fMRI using minimum partial correlation
Inferring large-scale brain connectivity from spectral properties of the EEG
Inferring the physical connectivity of complex networks from their functional dynamics
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity
Inferring functional brain connectivity from field-potential oscillations in health and disease
Inferring structural connectivity using Ising couplings in models of neuronal networks
Inferring network connectivity using kinetic Ising models
Inferring neural connectivity from multiple spike trains
Neuronal couplings inferred by an efficient inverse Ising method
Inferring neuronal functional connectivity using dynamic Bayesian networks
Inferring causal connectivity from pairwise recordings and optogenetics
Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy
Inferring functional connectivity in fMRI using minimum partial correlation
Inferring large-scale brain connectivity from spectral properties of the EEG
Inferring the physical connectivity of complex networks from their functional dynamics
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity