Mechanisms of self-organized quasicriticality in neuronal networks models

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 nonconservative but dissipative, we expect not exact criticality but self-organized quasicriticality (SOqC), where the system hovers around the critical point.

35 pages, 1 figure, in press Frontiers of Physics

References in corpus (9)

• Emergent complex neural dynamics
• Dynamical synapses causing self-organized criticality in neural networks
• Self-Organized Criticality model for Brain Plasticity
• Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
• Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks
• Feedback mechanisms for self-organization to the edge of a phase transition
• Dynamic range of hypercubic stochastic excitable media
• A simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis
• Deterministic excitable media under Poisson drive: power law responses, spiral waves and dynamic range