Cortical Composition Hierarchy Driven by Spine Proportion Economical Maximization or Wire Volume Minimization.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Structural Biology

Background:

• The cerebral cortex's computational capacity is linked to its structural composition.
• Empirical data reveal a consistent hierarchy in local cortical composition across species.
• These consistent fractions suggest optimality and importance for brain function.

Purpose of the Study:

• To model the local cortical circuit with coupled elements: neural wire, spines, astrocytes, and capillaries.
• To identify the underlying principle governing the invariant cortical composition.
• To test hypotheses including wire minimization and spine economy maximization.

Main Methods:

• Development of a mathematical model integrating neural wire, spines, astrocytes, and capillaries.
• Analysis of various wire minimization rules (length, surface area, volume, conduction delays).
• Investigation of a novel 'spine economy maximization' principle and combined meta-principles.

Main Results:

• Minimization of wire volume closely predicts empirical cortical fractions.
• Spine economy maximization provides equally good, more robust results.
• Combined principles offer marginal improvement, with spine economy dominance being key.

Conclusions:

• Wire volume minimization and spine economy maximization are strong candidates for explaining cortical composition.
• These principles suggest efficiency in local circuits, with wire volume potentially more primary than length or delays.
• Spine economy may be a significant factor in evolutionary brain design.