Simulate or emulate spiking neural networks on either of the two EBRAINS neuromorphic compute systems SpiNNaker and BrainScaleS. Models and simulation experiments can be described in a Python script using the PyNN API and submitted either through the EBRAINS Collaboratory website or via our web API (python client available). Results can be viewed via browser and downloaded as data files for analysis, making use e.g. of the data analysis capabilities EBRAINS offers. For real time SpiNNaker simulations, direct use in a neurorobotics simulated environment is also possible.

Our offer

  • Free, remote access to fast, powerful, programmable, brain-inspired computing devices
  • Access to two complementary, large-scale, custom-hardware neuromorphic computing systems: BrainScaleS (located in Heidelberg) and SpiNNaker (located in Manchester)
  • Enables high-speed, low-energy simulations or emulations of simplified spiking neural networks with synaptic plasticity
  • PyNN API for simulator-independent specification of neuronal network models



BrainScaleS System

The BrainScaleS system is based on physical (analogue or mixed-signal) emulations of neuron, synapse and plasticity models with digital connectivity, running up to ten thousand times faster than real time.


SpiNNaker System

The SpiNNaker system is based on numerical models running in real time on custom digital multicore chips using the ARM architecture.

Target Users

The neuromorphic computing systems should be of interest to researchers in multiple fields, including computational neuroscience and machine learning. Platform users will be able to study network implementations of their choice, including simplified versions of brain models developed by use of the EBRAINS Simulation services or generic circuit models based on theoretical work.

The platform also offers industry researchers and technology developers the possibility to experiment with and test applications based on state-of-the-art neuromorphic devices and systems.

Neuromorphic systems can offer higher speed (real-time or accelerated) and lower energy consumption than traditional HPC resources. The accelerated systems are particularly suited for investigation of plasticity and learning, enabling simulation of hours or days of biological time in only seconds or minutes.

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