Speaker
Description
NESTML is a domain-specific modeling language for neuron models and synaptic plasticity rules [1]. It is designed to support researchers in computational neuroscience by allowing them to specify models in a precise and intuitive way. These models can subsequently be used in dynamical simulations of small or large-scale spiking neural networks, using high-performance simulation code generated by the NESTML toolchain. The code extends a simulation platform (such as NEST [2]) with new and easy-to-specify neuron and synapse models, formulated in NESTML.
NESTML was originally developed for NEST simulations to be executed on CPUs; here we extend it with support for GPU-based simulation for the NEST-GPU target platform [3]. We demonstrate our approach with code generation for a balanced random network of integrate-and-fire neurons with postsynaptic currents in the form of decaying exponential kernels. The dynamics of the network are solved using exact integration [4]. We validate the results through a comparison of statistical properties of the network dynamics against those obtained from NEST running on CPUs as a reference.
By virtue of using NESTML, neuroscientists have to write the model code only once and have the same models run on multiple target platforms without having to write any additional code. This alleviates the need for neuroscientists to write CUDA/C++ code for GPUs, with its attendant pitfalls. This improves model reuse and scientific reproducibility.
References
[1] https://nestml.readthedocs.org/
[2] Gewaltig & Diesmann, Scholarpedia 2(4), 2007
[3] Golosio et al., Frontiers in Computational Neuroscience, 2021
[4] Rotter & Diesmann, Biological Cybernetics 81, 1999
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | Models and applications |
| Keywords | models, simulation, gpu |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
