Dynamic core theory
Explanation
Gerald Edelman and Giulio Tononi, in A Universe of Consciousness (2000), proposed the dynamic core hypothesis, a direct precursor of IIT. Their central intuition: consciousness is not a property of isolated brain regions, but of a functional cluster of neurons in rapid mutual interaction, forming a dynamic core that constantly reconfigures.
This core is not structural but functional. The same neurons may or may not form part of the core at different moments, depending on which others they are communicating with. What defines the core is a relational property: a set of neurons belongs to the core if they interact more among themselves than with external neurons, on time scales of hundreds of milliseconds.
The core has two key properties: high differentiation (it can take on many distinct states, encoding many possible experiences) and high integration (all its states are experienced as a unity, not as separate elements). This duality would later be quantified through the mathematical formula Φ in IIT.
Edelman, a Nobel laureate for his work in immunology, saw consciousness as a product of neuronal Darwinism: during development and learning, neuronal circuits are selected by their functional success, giving rise to a highly individualised brain. Consciousness emerges from the dynamic play between multiple selected neural maps that continually re-enter each other.
This theory connects with empirical observations. Anaesthesia and deep sleep disconnect the dynamic core (neurons remain active but no longer form an integrated functional cluster). Pathologies such as the persistent vegetative state show brains with activity but without adequate functional integration. Fragmentation of the core predicts loss of consciousness.
The dynamic core theory is historically important as a bridge between the early neurobiological theories (Crick and Koch on gamma binding) and the later mathematical theories (IIT). It established the fundamental principle that consciousness is a property of integrated-differentiated processes in time, not of individual isolated neurons or areas.
Strengths
- Direct precursor of IIT with similar intuitions.
- Empirical support in measurements of complexity.
- Integrates anatomy (thalamocortical loops) with dynamics.
- Coherent with neuronal Darwinism.
Main critiques
- Definition of 'core' not entirely precise.
- Practical problem of measuring complexity.
- Does not solve the hard problem.
- Overlaps with IIT, with which it competes for priority.