This title appears in the Scientific Report :
2022
From single-cell modeling to large-scale network dynamics with NEST Simulator
From single-cell modeling to large-scale network dynamics with NEST Simulator
NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial provides hands-on experience in building and simulating neur...
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Personal Name(s): | Linssen, Charl (Corresponding author) |
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Korcsak-Gorzo, Agnes / Albers, Jasper / Babu, Pooja / Böttcher, Joshua / Mitchell, Jessica / Wybo, Willem / Bruchertseifer, Jens / Spreizer, Sebastian / Terhorst, Dennis | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Jülich Supercomputing Center; JSC Computational and Systems Neuroscience; IAS-6 Jara-Institut Brain structure-function relationships; INM-10 |
Imprint: |
2022
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Conference: | 31st Annual Computational Neuroscience Meeting, CNS*2022, Melbourne (Australia), 2022-07-16 - 2022-07-20 |
Document Type: |
Conference Presentation |
Research Program: |
Human Brain Project Specific Grant Agreement 3 Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups Emerging NC Architectures Neuroscientific Foundations Digitization of Neuroscience and User-Community Building SimLab Neuroscience |
Publikationsportal JuSER |
NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial provides hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most efficiently. Participants do not have to install software as all tools can be accessed via the cloud.First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze abalanced two-population network.The model is then exported to a Jupyter notebook and endowed with a data-driven spatial connectivity profile of the cortex, enabling us to study the propagation of activity. Then, we make the synapses in the network plastic and let the network learn a reinforcement learning task, whereby the learning rule goes beyond pre-synaptic and post-synaptic spikes by addinga dopamine signal as a modulatory third factor. NESTML [3] makes it easy to express this and other advanced synaptic plasticity rules and neuron models, and automatically translates them into fast simulation code.More morphologically detailed models, with a large number of compartments and custom ion channels and receptor currents, can also be defined using NESTML. We first implement a simple dendritic layout and use it to perform a sequence discrimination task. Next, we implement a compartmental layout representing semi-independent subunits and recurrentlyconnect several such neurons to elicit an NMDA-spike driven network state. |