Spiking Neural Network (SNN) Frameworks

SpikingJelly

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

snnTorch

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

Maintained by

Jason Eshraghian

NIR Support

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BindsNET

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

Brian

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

Nengo

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

Maintained by

Trevor Bekolay

NIR Support Hardware Support

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Norse

Exploits bio-inspired neural components, sparse and event-driven, expands PyTorch with primitives for bio-inspired neural components.

Maintained by

Jens E. Pedersen, Christian Pehle

NIR Support

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Lava

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

NEST

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

GeNN

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

Tonic

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

Maintained by

Gregor Lenz, Fabrizio Ottati

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Spyx

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

Sinabs

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

AEStream

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

Maintained by

Jens E. Pedersen

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CARLsim

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

AEDAT

A Python package to decode AEDAT 4 files from event cameras with a Rust implementation for speed.

Maintained by

Alexandre Marcireau

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Expelliarmus

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

Maintained by

Fabrizio Ottati

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mlGeNN

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

jaxsnn

Event-based training of spiking neural networks with support for BrainScaleS-2 hardware-in-the-loop based on JAX.

Rockpool

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

Maintained by

Dylan Muir

NIR Support Hardware Support

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hxtorch

Training spiking neural networks with BrainScaleS-2 hardware-in-the-loop support based on PyTorch.

PyNN.brainscales2

Spiking neural networks with complex plasticity on BrainScaleS-2 neuromorphic hardware.