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.
Built on top of PyTorch, used for simulating SNNs, geared towards ML and reinforcement learning.
Open-source DL framework for SNN based on PyTorch, with documentation in English and Chinese.
Focuses on gradient-based training of SNNs, based on PyTorch for GPU acceleration and gradient computation.
Free, open-source simulator for SNNs, written in Python, focusing on ease of use and flexibility.
Python package for building, testing, deploying neural networks, supporting many backends for SNN simulation.
Exploits bio-inspired neural components, sparse and event-driven, expands PyTorch with primitives for bio-inspired neural components.
Simulator for SNN models focusing on dynamics, size, structure of neural systems, not on individual neuron morphology.
Framework for developing neuro-inspired applications, mapping them to neuromorphic hardware.
PyTorch-based DL library for SNNs, focusing on simplicity, fast training, extendability, and vision models.
Machine learning library for SNN applications, supports GPU, TPU, CPU acceleration, and neuromorphic compute hardware deployment.
GPU-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
Compact SNN package on DeepMind's Haiku library, based on JAX for JIT compilation on GPUs and TPUs.