ArticlePLoS computational biology2011
How structure determines correlations in neuronal networks.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 2 of them syntheses that pooled it.
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Who cites it
115 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods.Brain connectivity · 2023Pooled it
- Gray matter alterations in chronic pain: A network-oriented meta-analytic approach.NeuroImage. Clinical · 2014Pooled it
- Strong and localized recurrence controls the dimensionality of neural activity across brain areas.Nature neuroscience · 2026Article
- Article
- Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026Article
- CA1 Engram Cell Dynamics Before and After Learning.bioRxiv : the preprint server for biology · 2026Article
- Optimizing the in vitro neuronal microenvironment to mitigate phototoxicity in live-cell imaging.Stem cell research & therapy · 2025Article
- From Chaos to Coherence: Effects of High-Order Synaptic Correlations on Neural Dynamics.ArXiv · 2025Article
- Optical Recordings of Unitary Synaptic Connections Reveal High and Random Local Connectivity between CA3 Pyramidal Cells.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Modeling neuron-astrocyte interactions in neural networks using distributed simulation.PLoS computational biology · 2025Article
- Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cooperative coding of continuous variables in networks with sparsity constraint.PLoS computational biology · 2025Article
- Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model.bioRxiv : the preprint server for biology · 2025Article
- Coarse-Grained Hawkes Processes.Entropy (Basel, Switzerland) · 2025Article
- Sparse connectivity enables efficient information processing in cortex-like artificial neural networks.Frontiers in neural circuits · 2025Article
- Biological detail and graph structure in network neuroscience.Frontiers in network physiology · 2025Review
- The mechanics of correlated variability in segregated cortical excitatory subnetworks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Top-down modulation in canonical cortical circuits with short-term plasticity.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Anticipating regime shifts by mixing early warning signals from different nodes.Nature communications · 2024Article
- Analytic relationship of relative synchronizability to network structure and motifs.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
55 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.