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Self-organization in balanced state networks by STDP and homeostatic plasticity
Felix Effenberger1 and Juergen Jost1 Anna Levina2
1Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany 2Bernstein Center for Computational Neuroscience Goettingen, Germany

Cortical networks are characterized by inhomogeneities in structure and dynamical properties [1] and excitatory cortical cells were shown to form fine-scale subnetworks [2, 3]. These properties seem to play an important role in their functioning [4]. In a previous study [5], we presented a simulation study of a balanced state network where the interplay of different forms of synaptic plasticity lead to the emergence of long-tailed weight-distributions from an initially homogeneous state. Additionally, we observed that a small fraction of excitatory neurons that we call driver neurons develop predominantly strong outgoing synapses. Apart from having a strong outgoing connections, they are also distinguished by elevated firing rates and form subnetworks with an increased degree of connectivity, similar to some cortical subnetworks found in recent experiments [2,3]. Here, we investigate analytically and numerically the nature of the interaction of the different plasticity mechanisms that allow for the self-organized development of driver neurons.

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