Although contemporary theories of consciousness offer rich conceptual insights, the field remains fragmented, largely because few proposals provide a mechanistic testable account of how neural dynamics give rise to conscious experience.

The talk will present a unifying perspective grounded in critical brain dynamics, the hypothesis that cortical networks operate near a critical transition point in their dynamics. I will describe different types of critical transitions and outline the computational advantages of near-criticality. These principles will be illustrated through concrete examples from computational models. I will also address the inherent risks of operating near a critical point and discuss the role of neuroplasticity in stabilizing and regulating such dynamics.

Building on these foundations, I will introduce the ConCrit framework, which proposes that proximity to criticality provides a mechanistic basis for several leading theories of consciousness. I will review empirical evidence from anesthesia, sleep, psychedelics, disorders of consciousness, and related domains, supporting the prediction that conscious experience diminishes as the brain moves away from near-critical regimes.

Finally, I will discuss broader implications of the ConCrit framework, such as alterations in time perception in altered states of consciousness, and outline key limitations and open questions.

References:
Algom, I., & Shriki, O. (2025). The ConCrit Framework: Critical Brain Dynamics as a Unifying Mechanistic Framework for Theories of Consciousness. Neuroscience & Biobehavioral Reviews.
Yellin, D., Siegel, N., Malach, R., & Shriki, O. (2025). Adaptive proximity to criticality underlies amplification of ultra-slow fluctuations during free recall. PLOS Computational Biology, 21(10).
Shriki, O., & Yellin, D. (2016). Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neural Network. PLOS Computational Biology, 12(2).