Functional optimality of the sulcus pattern of the human brain

Area of Science:

• Neuroscience
• Mathematical Biology
• Computational Neuroscience

Background:

• The human brain's function emerges from information spread across its neural network.
• Brain capacity is linked to its information transmission rate.
• Understanding neural information flow is key to brain function.

Purpose of the Study:

• To develop a mathematical model for information transmission in the human brain's neural network.
• To test the hypothesis that the brain's sulcus pattern maximizes information transmission rates.
• To explore the relationship between neural network structure and processing capacity.

Main Methods:

• Developed a mathematical model based on potential theory for information transmission.
• Utilized the Steklov eigenvalue problem to define transmission modes and rates.
• Conducted numerical experiments on a spherical domain with varying surface slits (sulci).

Main Results:

• Identified preferred and quantized transmission modes corresponding to eigenfunctions.
• Reciprocal eigenvalues quantify the brain's information transmission rates.
• Demonstrated that introducing sulci (cuts) increases overall information transmission rates in numerical models.

Conclusions:

• The brain's sulcus pattern may have evolved to optimize information transmission efficiency.
• Mathematical modeling provides insights into the structural basis of neural computation.
• Potential theory and eigenvalue problems offer a framework for understanding brain capacity.