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

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.