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Related Experiment Video

Updated: May 27, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Scalable network emulation on analog neuromorphic hardware.

Elias Arnold¹, Philipp Spilger¹, Jan V Straub¹

¹European Institute for Neuromorphic Computing, Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, Germany.

Frontiers in Neuroscience

|February 20, 2025

Summary

This summary is machine-generated.

A new software feature enables partitioned emulation of large spiking neural networks on the BrainScaleS-2 neuromorphic system. This allows training bigger deep neural networks than the hardware physically supports, advancing neuromorphic computing.

Keywords:

accelerator abstraction modeling neuromorphic spiking neural networks virtualization

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Area of Science:

Neuromorphic Engineering
Computational Neuroscience
Artificial Intelligence

Background:

The BrainScaleS-2 platform offers accelerated neuromorphic computing.
Emulating large-scale spiking neural networks (SNNs) faces hardware size constraints.
Deep SNNs are crucial for advanced AI tasks.

Purpose of the Study:

To introduce a software feature for partitioned emulation of large-scale SNNs on BrainScaleS-2.
To enable training of SNNs exceeding single-chip physical limitations.
To facilitate performance evaluation of scaled neuromorphic systems.

Main Methods:

Developed a novel software feature for partitioned SNN emulation.
Implemented sequential model emulation on undersized neuromorphic resources.
Trained deep SNN models on MNIST and EuroSAT datasets exceeding BrainScaleS-2 size.

Main Results:

Successfully demonstrated partitioned emulation of large-scale SNNs.
Trained deep SNN models larger than the physical BrainScaleS-2 substrate.
Validated the approach using MNIST and EuroSAT datasets.

Conclusions:

The software feature enables emulation and training of SNNs larger than the physical hardware.
This facilitates accurate performance evaluation for future scaled neuromorphic systems.
Advances the development and understanding of large-scale SNNs and neuromorphic computing.