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The small world coefficient 4.8 ± 1 optimizes information processing in 2D neuronal networks.

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Area of Science:

  • Neuroscience
  • Network Science
  • Information Theory

Background:

  • Small world networks exhibit unique properties, with growing evidence suggesting optimized communication. However, quantitative estimates of information enhancement in these networks are limited.
  • Understanding information flow in complex systems like the brain is crucial for neuroscience and network theory.

Purpose of the Study:

  • To quantitatively estimate the efficiency of information transport in small world networks.
  • To investigate how network topology and stimulus characteristics influence information processing capacity.

Main Methods:

  • A brain model simulating neurons as agents integrating and transmitting signals.
  • Shannon Information Entropy was used to decode signals and compute transported information.
  • Numerical simulations varied network small-worldness, stimulus length, and frequency.

Main Results:

  • Information communication can be enhanced up to 100 times in small world networks compared to unstructured systems.
  • Information processing capacity increases with network small-worldness up to a threshold value.
  • Performance degrades beyond this threshold, indicating no benefit from excessive network connectivity.

Conclusions:

  • Small world networks can significantly enhance information transmission efficiency.
  • The concept of 'exordic systems' is introduced, defining systems topologically optimized for efficient information transfer.