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| 001 | 865395 | ||
| 005 | 20240313103124.0 | ||
| 024 | 7 | _ | |a 2128/24851 |2 Handle |
| 037 | _ | _ | |a FZJ-2019-04880 |
| 100 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Conditions for wave trains in spiking neural networks |
| 260 | _ | _ | |c 2019 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1570537778_4590 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 500 | _ | _ | |a 36 pages, 8 figures, 4 tables |
| 520 | _ | _ | |a Spatiotemporal 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 employ linear stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains 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, wave trains 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. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |x 0 |f POF III |
| 536 | _ | _ | |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |c PHD-NO-GRANT-20170405 |x 1 |
| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |x 2 |f SMHB |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |x 3 |f H2020-Adhoc-2014-20 |
| 536 | _ | _ | |a DFG project 233510988 - Mathematische Modellierung der Entstehung und Suppression pathologischer Aktivitätszustände in den Basalganglien-Kortex-Schleifen (233510988) |0 G:(GEPRIS)233510988 |c 233510988 |x 4 |
| 536 | _ | _ | |a ERS Seed Fund (ZUK2) - Exploratory Research Space: Seed Fund (2) als Anschubfinanzierung zur Erforschung neuer interdisziplinärer Ideen (ZUK2-SF) |x 5 |c ZUK2-SF |0 G:(DE-82)ZUK2-SF |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |x 6 |f H2020-SGA-FETFLAG-HBP-2017 |
| 536 | _ | _ | |a Helmholtz Young Investigators Group (HGF-YoungInvestigatorsGroup) |0 G:(DE-HGF)HGF-YoungInvestigatorsGroup |c HGF-YoungInvestigatorsGroup |x 7 |
| 700 | 1 | _ | |a Korvasová, Karolína |0 P:(DE-Juel1)162473 |b 1 |
| 700 | 1 | _ | |a Schuecker, Jannis |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Hagen, Espen |0 P:(DE-Juel1)164166 |b 3 |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 4 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 5 |
| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 6 |
| 856 | 4 | _ | |u https://arxiv.org/abs/1801.06046v2 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/865395/files/1801.06046v2.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/865395/files/1801.06046v2.pdf?subformat=pdfa |x pdfa |y OpenAccess |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)144174 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)144806 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
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| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
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| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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