ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2014
Perspective: network-guided pattern formation of neural dynamics.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- NIPS: Network Inference with Partial State measurements using forced-delay embedding.PNAS nexus · 2026Article
- Highly reconfigurable neuronlike conductive networks through nanophase structure engineering.Nature communications · 2025Article
- Active Inference in Psychology and Psychiatry: Progress to Date?Entropy (Basel, Switzerland) · 2024Review
- Energy-guided synapse coupling between neurons under noise.Journal of biological physics · 2023Article
- Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography.Brain sciences · 2022Article
- The brainstem connectome database.Scientific data · 2022Article
- NewFrontiers in physiology · 2022Article
- Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior.Cognitive, affective & behavioral neuroscience · 2019Review
- Self-assembly of biological networks via adaptive patterning revealed by avian intradermal muscle network formation.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Heterogeneity and Delayed Activation as Hallmarks of Self-Organization and Criticality in Excitable Tissue.Frontiers in physiology · 2019Article
- What We Know About the Brain Structure-Function Relationship.Behavioral sciences (Basel, Switzerland) · 2018Review
- Toward a theory of coactivation patterns in excitable neural networks.PLoS computational biology · 2018Article
- Connectivity and complex systems: learning from a multi-disciplinary perspective.Applied network science · 2018Review
- Critical and Supercritical Spatiotemporal Calcium Dynamics in Beta Cells.Frontiers in physiology · 2017Article
- Article
- Is the brain really a small-world network?Brain structure & function · 2016Article
- Self-organization of network dynamics into local quantized states.Scientific reports · 2016Article
- A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks.Scientific reports · 2015Article
- Complex network theory and the brain.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2014Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.
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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.