Gast ::
| Hauptseite > Publikationsdatenbank > Computational modeling of neuron-astrocyte interactions in large neural populations using the NEST simulator > print |
| Hauptseite > Publikationsdatenbank > Computational modeling of neuron-astrocyte interactions in large neural populations using the NEST simulator > print |
| 001 | 908965 | ||
| 005 | 20240313103131.0 | ||
| 024 | 7 | _ | |a 2128/31632 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-02921 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Aćimović, Jugoslava |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a 31st Annual Computational Neuroscience Meeting |g CNS*2022 |c Melbourne |d 2022-07-16 - 2022-07-20 |w Australia |
| 245 | _ | _ | |a Computational modeling of neuron-astrocyte interactions in large neural populations using the NEST simulator |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1661152936_5165 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Astrocytes,the most abundant glial type in the cortex, interact with neighboring synapses,neurons and glia through complex cellular machinery (Bazargani et al., 2016).Astrocytes form mostly nonoverlapping microdomains, and a single suchmicrodomain can be reached by several hundreds of neurons and as many as~100,000 synapses (Zisis et al., 2021). Experimental studies have demonstratedcoordinated neuronal and astrocytic activity invivo (Lines et al., 2020). Computational methods can help to integratethe data on cellular mechanisms and structural organization of the corticaltissue, and to explore how neuron-astrocyte interactions modulatepopulation-level activity. In the past two decades, the number of publishedcomputational models that include some form of neuron-astrocyte interaction hasbeen steadily increasing (Manninen et al., 2018; Manninen, Aćimović etal., 2018). The majority of the published models was implemented in custom madecode that is often not publicly available. Implementing these models in wellestablished open source simulation tools improves reproducibility of theresults and sharing of the models (Manninen et al., 2018; Manninen, Acimovic etal., 2018). Two earlier efforts to develop open source tools for simulation ofneuronal and glial networks include Arachne (Aleksin et al., 2017), and animplementation in the Brian simulator (Stimberg et al., 2019). We developed a new solution for efficient simulationof large heterogeneous populations of neurons and astrocytes implemented as a module in theNEST simulator (https://www.nest-simulator.org/). We first extended theconcept of a synapse in NEST to include interaction between three compartments,pre- and postsynaptic neurons and the neighboring astrocytic compartment. Next,we developed new method to establish efficiently interactions within a largeheterogeneous cellular population of neurons and astrocytes. Finally, we testedthe new tool by analyzing spontaneous activity regimes in medium-size networkscomposed of several hundreds of cells. In summary, we present a new module for NEST simulator that supports reproducible, open access and efficient development of computational models for large heterogeneous populations of neurons and astrocytes. |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 700 | 1 | _ | |a Jiang, Han-Jia |0 P:(DE-Juel1)176594 |b 1 |u fzj |
| 700 | 1 | _ | |a Stapmanns, Jonas |0 P:(DE-Juel1)171475 |b 2 |u fzj |
| 700 | 1 | _ | |a Manninen, Tiina |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Lehtimäki, Mikko |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Linne, Marja-Leena |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 6 |u fzj |
| 700 | 1 | _ | |a van Albada, Sacha J. |0 P:(DE-Juel1)138512 |b 7 |e Corresponding author |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/908965/files/Acimovic_Jiang_etal_2022_v7.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:908965 |p openaire |p open_access |p VDB |p driver |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)176594 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)171475 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)144174 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)138512 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5231 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 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 poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|