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000842906 005__ 20240313103124.0
000842906 0247_ $$2arXiv$$aarXiv:1801.06046
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000842906 037__ $$aFZJ-2018-01079
000842906 1001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b0$$eCorresponding author$$ufzj
000842906 245__ $$aConditions for traveling waves in spiking neural networks
000842906 260__ $$c2018
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000842906 500__ $$a42 pages, 12 figures
000842906 520__ $$aSpatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we derive conditions for the existence of spatial and temporal oscillations and periodic traveling waves using linear stability analysis. We first prove that periodic traveling waves cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, traveling waves emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons.
000842906 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x0
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000842906 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000842906 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x3
000842906 536__ $$0G:(GEPRIS)233510988$$aDFG project 233510988 - Mathematische Modellierung der Entstehung und Suppression pathologischer Aktivitätszustände in den Basalganglien-Kortex-Schleifen (233510988)$$c233510988$$x4
000842906 536__ $$0G:(DE-82)ZUK2-SF$$aERS Seed Fund (ZUK2) - Exploratory Research Space: Seed Fund (2) als Anschubfinanzierung zur Erforschung neuer interdisziplinärer Ideen (ZUK2-SF)$$cZUK2-SF$$x5
000842906 588__ $$aDataset connected to arXivarXiv
000842906 7001_ $$0P:(DE-Juel1)162473$$aKorvasová, Karolína$$b1$$ufzj
000842906 7001_ $$0P:(DE-HGF)0$$aSchuecker, Jannis$$b2
000842906 7001_ $$0P:(DE-Juel1)164166$$aHagen, Espen$$b3$$ufzj
000842906 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b4$$ufzj
000842906 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b5$$ufzj
000842906 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b6$$ufzj
000842906 8564_ $$uhttps://arxiv.org/abs/1801.06046
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000842906 9141_ $$y2018
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000842906 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
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