On Markov blankets and hierarchical self-organisation

Area of Science:

• Theoretical biology
• Computational neuroscience
• Systems biology

Background:

• Biological systems exhibit self-organization, forming complex structures across scales.
• Markov blankets define boundaries between internal and external states, crucial for system integrity.
• Understanding self-organization is key to explaining life's emergent properties.

Purpose of the Study:

• To investigate if Markov blankets can self-assemble hierarchically in biological systems.
• To model the emergence of self-organizing structures using computational simulations.
• To provide a theoretical framework for autopoiesis and self-evidencing behavior.

Main Methods:

• Simulations based on variational free energy minimization.
• Modeling ensembles of microscopic elements with prior beliefs about macroscopic Markov blankets.
• Analyzing emergent structures under short-range signaling conditions.

Main Results:

• Hierarchical self-organization of Markov blankets was demonstrated through simulations.
• Emergent structures closely resemble natural biological formations like cells and organelles.
• The model shows that collective beliefs drive the self-assembly of organizational levels.

Conclusions:

• Markov blankets can self-assemble into hierarchical structures, mirroring biological organization.
• This framework offers a potential explanation for self-evidencing and autopoietic behavior in life.
• The findings support a computational approach to understanding fundamental biological processes.