Spiking Neural Network (SNN) Frameworks

Discover essential SNN frameworks for neuromorphic software development.

  • Built on top of PyTorch, used for simulating SNNs, geared towards ML and reinforcement learning.

    Maintained by

    Hananel Hazan

  • Open-source DL framework for SNN based on PyTorch, with documentation in English and Chinese.

    Maintained by

    Wei Fang

  • Focuses on gradient-based training of SNNs, based on PyTorch for GPU acceleration and gradient computation.

    Maintainer: Jason Eshraghian

    Maintained by

    Jason Eshraghian
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  • Free, open-source simulator for SNNs, written in Python, focusing on ease of use and flexibility.

    Maintained by

    Romain Brette, Marcel Stimberg, Dan Goodman

  • Python package for building, testing, deploying neural networks, supporting many backends for SNN simulation.

    Maintainer: Trevor Bekolay

    Maintained by

    Trevor Bekolay
    NIR Support Hardware Support
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  • Exploits bio-inspired neural components, sparse and event-driven, expands PyTorch with primitives for bio-inspired neural components.

    Jens E. Pedersen

    Maintained by

    Jens E. Pedersen, Christian Pehle

    NIR Support
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  • Simulator for SNN models focusing on dynamics, size, structure of neural systems, not on individual neuron morphology.

    Maintained by

    Jochen Martin Eppler

  • Framework for developing neuro-inspired applications, mapping them to neuromorphic hardware.

    Maintained by

    Intel NC team

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  • Simulator for SNN models focusing on networks, not on individual neuron morphology. Optimised for accelerated simulations on computational backends including NVIDIA GPUs.

    Maintained by

    James Knight

  • Tonic is a Python package for managing and transforming neuromorphic datasets.

  • PyTorch-based DL library for SNNs, focusing on simplicity, fast training, extendability, and vision models.

    Maintained by

    Sadique Sheik

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  • AEStream is a tool for transmitting event data efficiently, supporting diverse inputs/outputs and integrating with Python and C++ libraries.

  • Machine learning library for SNN applications, supports GPU, TPU, CPU acceleration, and neuromorphic compute hardware deployment.

    Maintainer: Dylan Muir

    Maintained by

    Dylan Muir
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  • GPU-accelerated library for simulating large-scale spiking neural network (SNN) models with high biologically realistic synaptic dynamics.

    Maintained by

    Jeff Krichmar

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  • Compact SNN package on DeepMind's Haiku library, based on JAX for JIT compilation on GPUs and TPUs.

    Maintainer: Kade Heckel

    Maintained by

    Kade Heckel
    NIR Support
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  • A Python package to decode AEDAT 4 files from event cameras with a Rust implementation for speed.

  • Expelliarmus decodes event camera data into NumPy arrays, supporting various formats and offering ease of use for researchers and developers.​

  • Framework for machine learning with SNNs built on the GeNN simulator. Focused on ease of use in combination with computational efficiency derived from GeNN.

    Maintained by

    James Knight

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