Neuromorphic Software Guide
Explore our curated collection of open source neuromorphic software resources.
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Neuromorphic Software
Delve into the evolution of neuromorphic hardware, uncovering its rich history, detailed specifications, and the brilliant developers behind groundbreaking projects. Discover key milestones, technical intricacies, and the visionary minds shaping the future of intelligent computing.
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 SupportSimulator for SNN models focusing on networks, not on individual neuron morphology. Optimised for accelerated simulations on computational backends including NVIDIA GPUs.
- Website: https://genn-team.github.io/
- Source Code: https://github.com/genn-team/genn
- License: LGPL-2.1
Tonic is a Python package for managing and transforming neuromorphic datasets.
- Website: https://tonic.readthedocs.io/
- Source Code: https://github.com/neuromorphs/tonic
- License: GPL-3.0
PyTorch-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 SupportAEStream is a tool for transmitting event data efficiently, supporting diverse inputs/outputs and integrating with Python and C++ libraries.
- Website: https://aestream.github.io/aestream
- Source Code: https://github.com/aestream/aestream
- License: MIT
Machine 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 SupportA Python package to decode AEDAT 4 files from event cameras with a Rust implementation for speed.
- Website: https://pypi.org/project/aedat/
- Source Code: https://github.com/neuromorphicsystems/aedat
- License: MIT
Expelliarmus decodes event camera data into NumPy arrays, supporting various formats and offering ease of use for researchers and developers.
- Website: https://expelliarmus.readthedocs.io/
- Source Code: https://github.com/open-neuromorphic/expelliarmus
- License: GPL-2.0
Framework for machine learning with SNNs built on the GeNN simulator. Focused on ease of use in combination with computational efficiency derived from GeNN.
- Website: https://ml-genn.readthedocs.io/en/latest/
- Source Code: https://github.com/genn-team/ml_genn
- License: LGPL-2.1
