Publication Date

2021

Journal or Book Title

Scientific Reports

Abstract

Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain's neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (integrate and fire) is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary growth spurts in brain size, as per punctuated equilibrium theories in evolutionary biology.

ISSN

2045-2322

DOI

https://doi.org/10.1038/s41598-021-82461-4

Volume

11

Issue

1

License

UMass Amherst Open Access Policy

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Funder

National Science Foundation/BRAIN Initiative [ECCS1533257]; Office of Naval ResearchOffice of Naval Research [N00014-09-1-0069]; National Academies of Science & Engineering (NAK-FICB8); W.M. Keck FoundationW.M. Keck Foundation

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