Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.
Open source
24 alternativesSinabs
Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.
About Sinabs
Sinabs is a Python library for development and implementation of Spiking Convolutional Neural Networks (SCNNs) based on PyTorch. It implements spiking equivalents of CNN layers and can import CNN models built in Torch to test their spiking equivalents. It supports inference on neuromorphic hardware and porting models to DYNAP-CNN chips and dev-kits.
Key features
- Develop Spiking Convolutional Neural Networks in Python
- Provides spiking equivalents of CNN layers
- Import Torch CNN models to test spiking equivalents
- Supports inference on neuromorphic hardware
- Port models to DYNAP-CNN chips and dev-kits
- Install via pip or conda-forge
Best for: Researchers and developers building PyTorch-based spiking neural networks for neuromorphic hardware.
Good for
- Built on PyTorch for spiking neural network work
- Published under Apache-2.0 license
- Installable via pip and conda-forge
Sinabs at a glance
- Website
- github.com/synsense/sinabs
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Sinabs FAQ
How do I install the stable release?
Use pip install sinabs or conda install -c conda-forge sinabs.
What license covers Sinabs?
Apache v2.0; see the LICENSE file for details.
Where can I report bugs or contribute changes?
Through the GitHub repository; discussion is on the #sinabs Discord channel.
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