TY  - CONF
AU  - Spreizer, Sebastian
AU  - Linssen, Charl
AU  - Babu, Pooja
AU  - Diesmann, Markus
AU  - Morrison, Abigail
AU  - Weyers, Benjamin
TI  - Rapid prototyping in spiking neural network modeling with NESTML and NEST Desktop
M1  - FZJ-2024-07401
PY  - 2024
AB  - NEST is a well-established open source simulator providing researchers in computational neuroscience with the ability to perform high-performance simulations of spiking neuronal networks. However, as the simulation kernel is written in C++ for performance reasons, this makes it challenging for researchers without a programming background to customize and extend the built-in neuron and synapse models.In order to satisfy both the need for high-performance simulation codes and a good user experience in terms of easy-to-use modeling of neurons and synapses, NESTML was created as a domain-specific language with an unambiguous syntax. Alongside the language itself, a toolchain was developed that parses the model, analyzes the underlying equations, and performs code generation. The code generated by NESTML can be used in simulations of brain activity on several platforms, in particular the NEST Simulator. However, specification of the network architecture requires using the NEST Python API, which still requires users to be skilled at programming. This poses a problem for beginners, as experience shows that they usually have little to no coding experience.This issue was addressed by the development of NEST Desktop, a graphical user interface that serves as an intuitive, programming-free interface to NEST. The interface is easily installed and accessed via an internet browser on the computers of individual users, or in cloud-based deployments, which have already been proven in the field in student courses at universities across Europe.Here, we demonstrate for the first time the integration of all three components: NEST Desktop, NESTML and NEST. As a result, researchers and students can customize existing models or develop new ones using NESTML, and have them instantly available to create networks using the graphical interface of NEST Desktop, before simulating them efficiently using NEST as a back-end. The combined strength of these components creates a low-barrier environment for rapid prototyping and exploration of neuron, synapse and network models.
T2  - Bernstein Conference 2024
CY  - 29 Sep 2024 - 2 Oct 2024, Frankfurt am Main (Germany)
Y2  - 29 Sep 2024 - 2 Oct 2024
M2  - Frankfurt am Main, Germany
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1034642
ER  -