Critical Avalanches and Subsampling in Map-based Neural Networks

摘要

We investigate the synaptic noise as a novel mechanism for creating criticalavalanches in the activity of neural networks. We model neurons and chemicalsynapses by dynamical maps with a uniform noise term in the synaptic coupling.An advantage of utilizing maps is that the dynamical properties (actionpotential profile, excitability properties, post synaptic potential summationetc.) are not imposed to the system, but occur naturally by solving the systemequations. We discuss the relevant neuronal and synaptic properties to achievethe critical state. We verify that networks of excitatory by rebound neuronswith fast synapses present power law avalanches. We also discuss the measuringof neuronal avalanches by subsampling our data, shedding light on theexperimental search for Self-Organized Criticality in neural networks.