TY  - CONF
AU  - Linssen, Charl
AU  - Korcsak-Gorzo, Agnes
AU  - Albers, Jasper
AU  - Babu, Pooja
AU  - Böttcher, Joshua
AU  - Mitchell, Jessica
AU  - Wybo, Willem
AU  - Bruchertseifer, Jens
AU  - Spreizer, Sebastian
AU  - Terhorst, Dennis
TI  - From single-cell modeling to large-scale network dynamics with NEST Simulator
M1  - FZJ-2022-03260
PY  - 2022
AB  - 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.
T2  - 31st Annual Computational Neuroscience Meeting, CNS*2022
CY  - 16 Jul 2022 - 20 Jul 2022, Melbourne (Australia)
Y2  - 16 Jul 2022 - 20 Jul 2022
M2  - Melbourne, Australia
LB  - PUB:(DE-HGF)6
UR  - https://juser.fz-juelich.de/record/909578
ER  -