Charl Linssen, Sebastian Spreizer, Renato Duarte T1: NEST is established community software for the simulation of spiking neuronal network models capturing the full detail of biological network structures [1]. The simulator runs efficiently on a range of architectures from laptops to supercomputers [2]. Many peer-reviewed neuroscientific studies have used NEST as a simulation tool over the past 20 years. More recently, it has become a reference code for research on neuromorphic hardware systems [3].
This tutorial provides hands-on experience with recent improvements of NEST. In the past, starting out with NEST could be challenging for computational neuroscientists, as models and simulations had to be programmed using SLI, C++ or Python. NEST Desktop changes this: It is an entirely graphical approach to the construction and simulation of neuronal network models. It runs installation-free in the browser and has proven its value in several university courses. This opens the domain of NEST to the teaching of neuroscience for students with little programming experience.
NESTML complements this new interface by enhancing the development process of neuron and synapse models. Advanced researchers often want to study specific features not provided by models already available in NEST. Instead of having to turn to C++, using NESTML they can write down differential equations and necessary state transitions in the mathematical notation they are used to. These descriptions are then automatically processed to generate machine-optimised code.
After a quick overview of the current status of NEST and upcoming new functionality, the tutorial works through a concrete example [4] to show how the combination of NEST Desktop and NESTML can be used in the modern workflow of a computational neuroscientist.
Tutorial Website
Video stream link
The tutorial session will take place at the following link:
https://rwth.zoom.us/j/92601423152?pwd=QkpKRWNuQy9TVTJjODlsVVp0NUEwZz09Meeting-ID: 926 0142 3152
Password: 6vp&Yh
For more instructions please see our tutorial website.
References
- Gewaltig M-O & Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430
- Jordan J., Ippen T., Helias M., Kitayama I., Sato M., Igarashi J., Diesmann M., Kunkel S. (2018) Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics 12: 2
- Gutzen R., von Papen, M., Trensch G., Quaglio P. Grün S., Denker M. (2018) Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12 (90)
- Duarte R. & Morrison A. (2014). “Dynamic stability of sequential stimulus representations in adapting neuronal networks”, Front. Comput. Neurosci.
