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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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. A well-defined performance measure is required in order to improve its performance against new samples from that same task. L2L generalizes learning using a two loop structure .

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EBRAINS Software Distribution on HPC

EBRAINS is an open research infrastructure that gathers data, tools and computing facilities for brain-related research, built with interoperability at the core. 

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Last Modified: 05.12.2024