Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.
| Date | Stars |
|---|---|
| 2026-07-31 | 405 |
| 2026-08-05 | 405 |
| 2026-08-06 | 405 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# SpykeTorch High-speed simulator of convolutional spiking neural networks with at most one spike per neuron. <img src="https://raw.githubusercontent.com/miladmozafari/SpykeTorch/master/logo.png" alt="alt text" width=50%> SpykeTorch is a PyTorch-based simulator of convolutional spiking neural networks, in which the neurons emit at most one spike per stimulus. SpykeTorch supports STDP and Reward-modulated STDP learning rules. The current code is the early object oriented version of this simulator and you can find the documentation in docs folder in PDF format or in our lab website (http://cnrl.ut.ac.ir/SpykeTorch/doc/) in HTML format. Since SpykeTorch is fully compatible with PyTorch, you can easily use it if you know PyTorch. A tutorial is available in the paper titled "SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron" which introduces the SpykeTorch package (https://www.frontiersin.org/articles/10.3389/fnins.2019.00625/full). **IMPORTANT**: Current version of SpykeTorch does not support negative synaptic weights. To setup this package, you can install Anaconda or Miniconda ``` # Clone the repository git clone https://github.com/miladmozafari/SpykeTorch # Create a new conda environment conda create -n spyketorchproject python=3 conda activate spyketorchproject # Install all the dependencies pip install -r requirements.txt ``` ``` # Alternatively, one can just run the following command pip install git+https://github.com/miladmozafari/SpykeTorch.git ``` **Scripts info:** - [`MozafariShallow.py`](MozafariShallow.py): Reimplementation of the paper "First-Spike-Based Visual Categorization Using Reward-Modulated STDP" (https://ieeexplore.ieee.org/document/8356226/). - [`MozafariDeep.py`](MozafariDeep.py): Reimplementation of the paper "Bio-Inspired Digit Recognition Using Reward-Modulated Spike-Timing-Dependent Plasticity in Deep Convolutional Networks" (https://www.sciencedirect.com/science/article/abs/pii/S0031320319301906). - [`KheradpishehDeep.py`](KheradpishehDeep.py): Reimplementation of the paper "STDP-based spiking deep convolutional neural networks for object recognition" (https://www.sciencedirect.com/science/article/pii/S0893608017302903). - [`tutorial.ipynb`](tutorial.ipynb): A brief tutorial on designing, training, and evaluating a SNN with SpykeTorch.
Excerpt of 2,371 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ac726c66f5ac2fc2, llm:Repository description: 'High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.' (miladmozafari/SpykeTorch).
matched fp:ac726c66f5ac2fc2, llm:Repository description: 'High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.' (miladmozafari/SpykeTorch).
matched fp:ac726c66f5ac2fc2, llm:Repository description: 'High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.' (miladmozafari/SpykeTorch).