synsense/rockpool
A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.
Rockpool helps machine learning engineers develop specialized signal processing applications using spiking neural networks (SNNs). You can feed in time-series data or event streams, build your SNN models, train them with familiar deep learning frameworks like PyTorch or JAX, and then deploy them directly to neuromorphic hardware or run them in simulation. This is ideal for developers creating energy-efficient, event-driven AI solutions.
Used by 1 other package. Available on PyPI.
Use this if you are an ML engineer building signal processing applications that require the unique capabilities and efficiency of spiking neural networks, especially for deployment on neuromorphic hardware.
Not ideal if you are doing detailed simulations of biological neural networks or if your project doesn't specifically require spiking neural networks.
Stars
77
Forks
13
Language
Python
License
AGPL-3.0
Category
Last pushed
Feb 10, 2026
Commits (30d)
0
Dependencies
2
Reverse dependents
1
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