Spyx
A compact spiking neural network library built on JAX and Haiku, offering high-performance training via surrogate gradient descent and neuroevolution.
What is Spyx?
Spyx is a spiking neural network library built on JAX and Haiku, designed for high-performance training of SNNs. It supports surrogate gradient descent and neuroevolution methods, enabling efficient optimization of spiking models for neuromorphic computing and neuroscience research.
Researchers and developers working in neuromorphic computing, computational neuroscience, or machine learning who need efficient tools for training and experimenting with spiking neural networks.
Spyx offers a unique combination of PyTorch-like flexibility and custom CUDA kernel-level performance, making it ideal for prototyping custom neuron models while achieving state-of-the-art training speeds on GPU hardware.
Overview
Spyx: Spiking Neural Networks in JAX
Use Cases
Best For
- Training spiking neural networks with surrogate gradient methods
- Neuroevolution experiments for optimizing SNN architectures
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