| Autor: |
Linssen, Charl, Korcsak-Gorzo, Agnes, Albers, Jasper, Babu, Pooja, Böttcher, Joshua, Mitchell, Jessica, Wybo, Willem, Bruchertseifer, Jens, Spreizer, Sebastian, Terhorst, Dennis |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Zdroj: |
31st Annual Computational Neuroscience Meeting, CNS*2022, Melbourne, Australia, 2022-07-16-2022-07-20 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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