Mechanisms of Self-Organized Quasicriticality in Neuronal Network Models
Osame Kinouchi, Renata Pazzini, Mauro Copelli
Abstract
The critical brain hypothesis states that there are information processing advantages for neuronal networks working close to the critical region of a phase transition. If this is true, we must ask how the networks achieve and maintain this critical state. Here, we review several proposed biological mechanisms that turn the critical region into an attractor of a dynamics in network parameters like synapses, neuronal gains, and firing thresholds. Since neuronal networks (biological and models) are not conservative but dissipative, we expect not exact criticality but self-organized quasicriticality, where the system hovers around the critical point.
Topics & Concepts
Self-organized criticalityComputer scienceDissipative systemCritical point (mathematics)AttractorBiological networkCriticalityBiological neural networkNeurosciencePhase transitionStatistical physicsPhysicsMathematicsBiologyMachine learningMathematical analysisQuantum mechanicsNuclear physicsCombinatoricsNeural dynamics and brain functionstochastic dynamics and bifurcationPhotoreceptor and optogenetics research