Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity

PLoS Computational Biology
Felix EffenbergerAnna Levina

Abstract

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.

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Citations

Apr 1, 2017·PLoS Computational Biology·Sergey G AleksinLeonid P Savtchenko
Apr 16, 2016·PLoS Computational Biology·Yotam Luz, Maoz Shamir
Jun 4, 2019·Network Neuroscience·Fabrizio DamicelliArnaud Messé
Dec 25, 2019·Biological cybernetics·Pau Vilimelis AceitunoJürgen Jost
Aug 16, 2016·PLoS Computational Biology·Neta Ravid Tannenbaum, Yoram Burak
Feb 29, 2020·Biological cybernetics·Christoph BauermeisterJochen Braun
Aug 24, 2017·Scientific Reports·Valentina PasqualeMichela Chiappalone
Mar 21, 2020·Frontiers in Computational Neuroscience·Samantha N SchummDavid F Meaney
Apr 10, 2019·PloS One·Patrick KraussClaus Metzner
Mar 19, 2021·Frontiers in Computational Neuroscience·Fabian Schubert, Claudius Gros

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