Spontaneous generation of selectable variation in the brain

Area of Science:

• Computational neuroscience
• Theoretical biology
• Systems neuroscience

Background:

• Selection models of behavior require stable, selectable variation within neural systems.
• Existing models often lack mechanisms for the spontaneous emergence of such diversity.

Purpose of the Study:

• To propose a mechanism for the spontaneous generation of stable neural diversity.
• To explore how local cell assembly interactions can lead to large-scale brain patterns.

Main Methods:

• Modeling cell assemblies with aligning and disaligning influences.
• Simulating interactions analogous to physical processes like magnetization and crystallization.
• Analyzing emergent patterns under different interaction distributions (random vs. lattice-based).

Main Results:

• Random interactions can lead to spin-glass-like states with spatially varied, temporally stable processing modes.
• Regular disalignment on a lattice, combined with aligning interactions, generates patterns resembling visual orientation columns.
• Emergent patterns are dynamically maintained and statistically determined, not genetically specified in detail.

Conclusions:

• Neural diversity can arise spontaneously through local cell assembly interactions.
• These emergent patterns can form stable, functional structures like orientation columns.
• This provides a framework for understanding the development of brain organization without exhaustive genetic programming.