Software

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NEST: The Neural Simulation Tool

NEST is a high performance simulator for large networks of spiking neurons, developed with a focus on reproducibility and correctness. It is optimised for single or few compartment neuron models and incorporates a wide range of synaptic plasticity models. NEST is used to investigate many aspects of function and dysfunction in neural systems.

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Arbor

Arbor is a performance portable library for the simulation of large networks of multi-compartment, morphologically detailed neurons on emerging HPC architectures. It is developed under an open development model by the Jülich Supercomputing Centre’s SimLab Neuroscience and the Swiss National Supercomputing Center (CSCS), in close collaboration with the neuroscientific community.

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HeAT

The SimLab Neuroscience contributes to the development of the Helmholtz Analytics Toolkit (HeAT), a flexible and seamless open-source software for high performance data analytics and machine learning.

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hpc4neuro Python library

In this project we are developing a library of various utilities that we factor out from other projects.

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Deep Learning software for Supercomputers

We contribute to improving the supercomputing infrastructure so that users can run distributed training and inference with minimal effort.

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Learning to Learn (L2L) on HPC

Learning to learn (L2L) is a specific method to improve learning performance. In a normal learning regime, a program is trained against samples from a single task.

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Last Modified: 08.07.2022