The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
The Non-Random Brain: Efficiency, Economy, and Complex Dynamics
Open accessAbstract
Modern anatomical tracing and imaging techniques are beginning to reveal the structural anatomy of neural circuits at small and large scales in unprecedented detail. When examined with analytic tools from graph theory and network science, neural connectivity exhibits highly non-random features, including high clustering and short path length, as well as modules and highly central hub nodes. These characteristic topological features of neural connections shape non-random dynamic interactions that occur during spontaneous activity or in response to external stimulation. Disturbances of connectivity and thus of neural dynamics are thought to underlie a number of disease states of the brain, and some evidence suggests that degraded functional performance of brain networks may be the outcome of a process of randomization affecting their nodes and edges. This article provides a survey of the non-random structure of neural connectivity, primarily at the large scale of regions and pathways in the mammalian cerebral cortex. In addition, we will discuss how non-random connections can give rise to differentiated and complex patterns of dynamics and information flow. Finally, we will explore the idea that at least some disorders of the nervous system are associated with increased randomness of neural connections.
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Cites
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Dynamic functional connectivity: Promise, issues, and interpretations
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Self-citationProbing and regulating dysfunctional circuits using deep brain stimulation
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Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics
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Resting-state functional connectivity in autism spectrum disorders: a review
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Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
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Metadata
- Authors
- Sporns, O.
- Venue
- Frontiers in Computational Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2011
- Volume
- 5
- Pages
- 5-5
- ISSN
- 1662-5188 (ISSN-L)
- OpenAlex
- W1974644418
- Cited by
- 233
- References
- 29
- FWCI
- 11.15
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
