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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.
Simulation of spiking neural networks (SNNs) using PyTorch.
| Date | Stars |
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| 2026-07-24 | 1687 |
| 2026-07-25 | 1687 |
| 2026-07-28 | 1687 |
| 2026-07-30 | 1687 |
| 2026-07-31 | 1690 |
| 2026-08-04 | 1690 |
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| 2026-08-06 | 1690 |
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<p align="center"><img width="25%" src="docs/logo.png"/></p> A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using [PyTorch](http://pytorch.org/) `Tensor` functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs, machine learning (ML) and reinforcement learning (RL) problems in the [Biologically Inspired Neural & Dynamical Systems (BINDS) lab](http://binds.cs.umass.edu/) and the Allen Discovery Center at Tufts University. Check out the [BindsNET examples](https://github.com/BindsNET/bindsnet/tree/master/examples) for a collection of experiments, functions for the analysis of results, plots of experiment outcomes, and more. Documentation for the package can be found [here](https://bindsnet-docs.readthedocs.io). [](https://github.com/BindsNET/bindsnet/actions/workflows/python-app.yml) [](https://github.com/BindsNET/bindsnet/actions/workflows/github-code-scanning/codeql) [](https://bindsnet-docs.readthedocs.io/?badge=latest) [](https://open-neuromorphic.org/neuromorphic-computing/) [](https://doi.org/10.5281/zenodo.20695115) ## Requirements - Python >=3.11,<3.14 (continuously tested on 3.11, 3.12, and 3.13) ## Setting things up ### Reproducible install For a byte-for-byte reproducible environment, install the pinned dependency set from the committed `poetry.lock`: ``` poetry install ``` Alternatively, the provided `Dockerfile` builds the full pinned stack (see *Using Docker* below). ## Using Pip To install the most recent stable release from the GitHub repository ``` pip install git+https://github.com/BindsNET/bindsnet.git ``` Or, to build the `bindsnet` package from source, clone the GitHub repository, change directory to the top level of this project, and issue ``` pip install . ``` Or, to install in editable mode (allows modification of package without re-installing): ``` pip install -e . ``` To install the packages necessary to interface with the [OpenAI gym RL environments library](https://github.com/openai/gym), follow their instructions for installing the packages needed to run the RL environments simulator (on Linux / MacOS). ### Using Docker [Link](https://hub.docker.com/r/hqkhan/bindsnet/) to Docker repository. We also provide a Dockerfile in which BindsNET and all of its dependencies come installed in. Issue ``` docker build . ``` at the top level directory of this project to create a docker image. To change the name of the newly built image, issue ``` docker tag <IMAGE_ID> <NEW_IMAGE_ID> ``` To run a container and get a bash terminal inside it, issue ``` docker run -it <NEW_IMAGE_ID> bash ``` ## Getting started To run a near-replication of the SNN from [this paper](https://www.frontiersin.org/articles/10.3389/fncom.2015.00099/full#), issue ``` cd examples/mnist python eth_mnist.py ``` There are a number of optional command-line arguments which can be passed in, including `--plot` (displays useful monitoring figures), `--n_neurons [int]` (number of excitatory, inhibitory neurons simulated), `--mode ['train' | 'test']` (sets network operation to the training or testing phase), and more. Run the script with the `--help` or `-h` flag for more information. A number of other examples are available in the `examples` directory that are meant to showcase BindsNET's functionality. Take a look, and let us know what you think! ## Runnin
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e3e09272210b36a8, topic:reinforcement-learning, readme:reinforcement learning
matched fp:e3e09272210b36a8, topic:pytorch
matched fp:e3e09272210b36a8, topic:simulation