Neuromorphic Software Guide
Explore our curated collection of open source neuromorphic software resources.
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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.
Maintained by
NIR SupportView DetailsBuilt on top of PyTorch, used for simulating SNNs, geared towards ML and reinforcement learning.
Maintained by
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.
Maintained by
NIR Support Hardware SupportView DetailsExploits bio-inspired neural components, sparse and event-driven, expands PyTorch with primitives for bio-inspired neural components.
Maintained by
NIR SupportView DetailsFramework for developing neuro-inspired applications, mapping them to neuromorphic hardware.
Maintained by
Intel NC team
NIR Support Hardware SupportView DetailsSimulator for SNN models focusing on dynamics, size, structure of neural systems, not on individual neuron morphology.
Maintained by
Jochen Martin Eppler
Simulator for SNN models focusing on networks, not on individual neuron morphology. Optimised for accelerated simulations on computational backends including NVIDIA GPUs.
Maintained by
Tonic is a Python package for managing and transforming neuromorphic datasets.
Maintained by
JAX-based library for spiking and rate models, biologically plausible learning, and reproducible neuroscience via the Model Museum.
Maintained by
Neural Adaptive Computing Laboratory (NAC Lab)
A graph-based intermediate representation for computational graphs of spiking neural networks, enabling interoperability across different simulators and hardware.
Maintained by
Jens E. Pedersen, Felix Bauer, Jason Eshraghian, Bernhard Vogginger
NIR Support Hardware SupportView DetailsCompact SNN package on DeepMind's Haiku library, based on JAX for JIT compilation on GPUs and TPUs.
Maintained by
NIR SupportView DetailsPyTorch-based DL library for SNNs, focusing on simplicity, fast training, extendability, and vision models.
Maintained by
Sadique Sheik
NIR Support Hardware SupportView DetailsAEStream is a tool for transmitting event data efficiently, supporting diverse inputs/outputs and integrating with Python and C++ libraries.
Maintained by
A domain-specific language and code generation toolchain for neuron and synapse models in spiking neural network simulation
Maintained by
Charl Linssen
Hardware SupportView DetailsGPU-accelerated library for simulating large-scale spiking neural network (SNN) models with high biologically realistic synaptic dynamics.
Maintained by
Jeff Krichmar
Hardware SupportView DetailsExpelliarmus decodes event camera data into NumPy arrays, supporting various formats and offering ease of use for researchers and developers.
Maintained by
A Python package to decode AEDAT 4 files from event cameras with a Rust implementation for speed.
Maintained by
PyTorch based library combining machine learning and neuroscience through spiking and biologically inspired neural dynamics.
Maintained by
Jérémie Gince
A platform for creating and training Spiking Neural Networks (SNNs), supporting various data types and neuromorphic processors.
Maintained by
Kaspersky
Hardware SupportView DetailsFramework 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
Event-based training of spiking neural networks with support for BrainScaleS-2 hardware-in-the-loop based on JAX.
Maintained by
Electronic Visions Group
NIR Support Hardware SupportView DetailsPython and Rust libraries to interact with event cameras in real-time.
Maintained by
A modern, fast, and friendly Python library for processing event-based camera data, with a focus on streaming and interoperability.
Maintained by
A PyTorch extension for the Neuromorphic Intermediate Representation (NIR), enabling seamless conversion of PyTorch-based SNN models to and from NIR.
Maintained by
NIR Support Hardware SupportView DetailsPython Toolkit for Spiking Neural Networks and general purpose Neuromorphic Computing
Maintained by
Neucom
Training spiking neural networks with BrainScaleS-2 hardware-in-the-loop support based on PyTorch.
Maintained by
Electronic Visions Group
NIR Support Hardware SupportView DetailsMachine learning library for SNN applications, supports GPU, TPU, CPU acceleration, and neuromorphic compute hardware deployment.
Maintained by
NIR Support Hardware SupportView DetailsSpiking neural networks with complex plasticity on BrainScaleS-2 neuromorphic hardware.
Maintained by
Electronic Visions Group
Hardware SupportView Details
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