Spiking Neural Network (SNN) Frameworks
Discover essential SNN frameworks for neuromorphic software development.
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SNN Frameworks
Explore essential Spiking Neural Network (SNN) frameworks tailored for the advancement of neuromorphic software development. This guide serves as a comprehensive resource to help researchers and developers navigate and choose frameworks that align with their objectives in the field of neuromorphic computing.
![Spiking Neural Network (SNN) Frameworks](/neuromorphic-computing/software/snn-frameworks/image_hu504580e4e0594bf3f7bf7b76af994632_212432_1280x720_crop_q90_h2_lanczos_smart1.webp)
Built on top of PyTorch, used for simulating SNNs, geared towards ML and reinforcement learning.
- Website: https://bindsnet-docs.readthedocs.io/
- Source Code: https://github.com/bindsnet/bindsnet
- License: AGPL-3.0
Open-source DL framework for SNN based on PyTorch, with documentation in English and Chinese.
- Website: https://spikingjelly.readthedocs.io
- Source Code: https://github.com/fangwei123456/spikingjelly
- License: unknown
Focuses on gradient-based training of SNNs, based on PyTorch for GPU acceleration and gradient computation.
- Website: https://snntorch.readthedocs.io
- Source Code: https://github.com/jeshraghian/snntorch
- License: MIT
NIR SupportFree, open-source simulator for SNNs, written in Python, focusing on ease of use and flexibility.
- Website: https://briansimulator.org/
- Source Code: https://github.com/brian-team/brian2
- License: custom
Python package for building, testing, deploying neural networks, supporting many backends for SNN simulation.
- Website: https://nengo.ai
- Source Code: https://github.com/nengo/nengo
- License: custom
NIR Support Hardware SupportExploits bio-inspired neural components, sparse and event-driven, expands PyTorch with primitives for bio-inspired neural components.
- Website: https://norse.github.io/norse/
- Source Code: https://github.com/norse/norse
- License: LGPL-3.0
NIR SupportSimulator for SNN models focusing on dynamics, size, structure of neural systems, not on individual neuron morphology.
- Website: https://www.nest-simulator.org/
- Source Code: https://github.com/nest/nest-simulator
- License: GPL-2.0
Framework for developing neuro-inspired applications, mapping them to neuromorphic hardware.
- Website: https://lava-nc.org/
- Source Code: https://github.com/lava-nc/lava
- License: custom
NIR Support Hardware SupportPyTorch-based DL library for SNNs, focusing on simplicity, fast training, extendability, and vision models.
- Website: https://sinabs.ai
- Source Code: https://github.com/synsense/sinabs
- License: AGPL-3.0
NIR Support Hardware SupportMachine learning library for SNN applications, supports GPU, TPU, CPU acceleration, and neuromorphic compute hardware deployment.
- Website: https://rockpool.ai
- Source Code: https://gitlab.com/synsense/rockpool
- License: AGPL-3.0
NIR Support Hardware SupportGPU-accelerated library for simulating large-scale spiking neural network (SNN) models with high biologically realistic synaptic dynamics.
- Website: https://uci-carl.github.io/CARLsim3/
- Source Code: https://github.com/UCI-CARL/CARLsim6
- License: MIT
Hardware SupportCompact SNN package on DeepMind's Haiku library, based on JAX for JIT compilation on GPUs and TPUs.
- Website: https://spyx.readthedocs.io
- Source Code: https://github.com/kmheckel/spyx
- License: MIT
NIR Support