The Cosmic Connectome: Our Universe is a Giant Brain, According to Scientists

by Alex Vikoulov [Posted November 17, 2020 06.00 pm PST]

A new research at the intersection of cosmology and neurobiology implies that diverse physical processes lead to similar levels of complexity and self-organization present in structures of scales.
 
An astrophysicist at the University of Bologna and a neurosurgeon at the University of Verona compared the network of neuronal cells in the human brain with the cosmic network of galaxies and found astounding similarities.
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In their paper ‘The quantitative comparison between the neuronal network and the cosmic web’ published in Frontiers in Physics, Franco Vazza, astrophysicist at the University of Bologna, and Alberto Feletti, neurosurgeon at the University of Verona, investigated the similarities between two of the most complex systems in existence: the cosmic web of galactic superclusters and the network of neuronal cells in the human brain.
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Despite the stupendous difference in scale between the two networks of roughly 27 orders of magnitude, the human brain and the composition of the cosmic connectome show similar levels of complexity and self-organization, according to the researchers.

The human brain functionality depends on its vast neural network which is estimated to contain nearly 70 billion neurons. In comparison, the observable universe constitutes a cosmic web of at least 100 billion galaxies. Within both systems, only 30% of their total masses are composed of galaxies and neurons that are arranged in long filaments between nodes. The rest of 70% of the composition of mass-energy plays a seemingly passive role: the brain’s water and the universe’s dark energy.

While comparing a computer simulation of the galactic connectome to sections of the cerebral cortex, the researchers aimed to observe how matter fluctuations scatter over such varied scales apart.

"We calculated the spectral density of both systems. This is a technique often employed in cosmology for studying the spatial distribution of galaxies," explains Franco Vazza. "Our analysis showed that the distribution of the fluctuation within the cerebellum neuronal network on a scale from 1 micrometer to 0.1 millimeters follows the same progression of the distribution of matter in the cosmic web but, of course, on a larger scale that goes from 5 million to 500 million light-years."

The two researchers also calculated some parameters characterizing both the neuronal network and the cosmic connectome: the average number of links in each node and the tendency of clustering several links in central nodes within both networks. 

"Once again, structural parameters have identified unexpected agreement levels. Probably, the connectivity within the two networks evolves following similar physical principles, despite the striking and obvious difference between the physical powers regulating galaxies and neurons," adds Alberto Feletti. "These two complex networks show more similarities than those shared between the cosmic web and a galaxy or a neuronal network and the inside of a neuronal body."

The remarkable results of this interdisciplinary study are urging the scientists to think that new and effective analysis techniques in both fields, cosmology and neurosurgery, will allow for a better understanding of the underlying dynamics of these two vital systems.

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READ MORE: F. Vazza et al, 2020, The Quantitative Comparison Between the Neuronal Network and the Cosmic Web, Frontiers in Physics, DOI: 10.3389/fphy.2020.525731

A. Tuynman, 2020, Universal Mind Revealed as Instantiated in a Multi-Layered Quantum Neural Network

A. Vikoulov, 2020, Ontological Holism: The Ultimate Reality of Self-Simulated Universe

​Keywords: ​cosmic connectome, cosmology, neurobiology, complexity, self-organization astrophysics, cosmic network, neuronal network, cosmic web, Frontiers in Physics, Franco Vazza, University of Bologna, Alberto Feletti, University of Verona, galactic superclusters, human brain, observable universe, dark energy

*Image: Shutterstock
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