Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity.
Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.
Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany.
Felix
F
Effenberger
Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany.
Jürgen
J
Jost
Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany; Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.
Anna
A
Levina
D019295:Computational Biology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D013569:Synapses / Q000502:physiology*