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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.
Tonic is a Python package for managing and transforming neuromorphic datasets.
PyTorch-based DL library for SNNs, focusing on simplicity, fast training, extendability, and vision models.
AEStream is a tool for transmitting event data efficiently, supporting diverse inputs/outputs and integrating with Python and C++ libraries.
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.
A Python package to decode AEDAT 4 files from event cameras with a Rust implementation for speed.
Expelliarmus decodes event camera data into NumPy arrays, supporting various formats and offering ease of use for researchers and developers.