{"id":21684609,"url":"https://github.com/nengo/pytorch-spiking","last_synced_at":"2025-04-12T07:52:03.902Z","repository":{"id":52215152,"uuid":"289266517","full_name":"nengo/pytorch-spiking","owner":"nengo","description":"Spiking neuron integration for PyTorch","archived":false,"fork":false,"pushed_at":"2025-03-18T17:53:32.000Z","size":8932,"stargazers_count":40,"open_issues_count":2,"forks_count":6,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-12T07:51:59.882Z","etag":null,"topics":["deep-learning","python","pytorch","spiking-neural-networks"],"latest_commit_sha":null,"homepage":"https://www.nengo.ai/pytorch-spiking/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nengo.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGES.rst","contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE.rst","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2020-08-21T12:39:44.000Z","updated_at":"2025-03-13T17:34:41.000Z","dependencies_parsed_at":"2024-01-19T22:27:49.750Z","dependency_job_id":"e8491d88-91b5-460b-93c5-abff5382e375","html_url":"https://github.com/nengo/pytorch-spiking","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fpytorch-spiking","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fpytorch-spiking/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fpytorch-spiking/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fpytorch-spiking/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nengo","download_url":"https://codeload.github.com/nengo/pytorch-spiking/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248537033,"owners_count":21120690,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","python","pytorch","spiking-neural-networks"],"created_at":"2024-11-25T16:16:21.732Z","updated_at":"2025-04-12T07:52:03.852Z","avatar_url":"https://github.com/nengo.png","language":"Python","funding_links":[],"categories":["Frameworks"],"sub_categories":[],"readme":".. image:: https://img.shields.io/pypi/v/pytorch-spiking.svg\n  :target: https://pypi.org/project/pytorch-spiking\n  :alt: Latest PyPI version\n\n.. image:: https://img.shields.io/pypi/pyversions/pytorch-spiking.svg\n  :target: https://pypi.org/project/pytorch-spiking\n  :alt: Python versions\n\n**************\nPyTorchSpiking\n**************\n\nPyTorchSpiking provides tools for training and running spiking neural networks\ndirectly within the PyTorch framework. The main feature is\n``pytorch_spiking.SpikingActivation``, which can be used to transform\nany activation function into a spiking equivalent. For example, we can translate a\nnon-spiking model, such as\n\n.. code-block:: python\n\n    torch.nn.Sequential(\n        torch.nn.Linear(5, 10),\n        torch.nn.ReLU(),\n    )\n\ninto the spiking equivalent:\n\n.. code-block:: python\n\n    torch.nn.Sequential(\n        torch.nn.Linear(5, 10),\n        pytorch_spiking.SpikingActivation(torch.nn.ReLU()),\n    )\n\nModels with SpikingActivation layers can be optimized and evaluated in the same way as\nany other PyTorch model. They will automatically take advantage of PyTorchSpiking's\n\"spiking aware training\": using the spiking activations on the forward pass and the\nnon-spiking (differentiable) activation function on the backwards pass.\n\nPyTorchSpiking also includes various tools to assist in the training of spiking models,\nsuch as `filtering layers\n\u003chttps://www.nengo.ai/pytorch-spiking/reference.html#module-pytorch_spiking.modules\u003e`_.\n\nIf you are interested in building and optimizing spiking neuron models, you may also\nbe interested in `NengoDL \u003chttps://www.nengo.ai/nengo-dl\u003e`_. See\n`this page \u003chttps://www.nengo.ai/pytorch-spiking/nengo-dl-comparison.html\u003e`_ for a\ncomparison of the different use cases supported by these two packages.\n\n**Documentation**\n\nCheck out the `documentation \u003chttps://www.nengo.ai/pytorch-spiking/\u003e`_ for\n\n- `Installation instructions\n  \u003chttps://www.nengo.ai/pytorch-spiking/installation.html\u003e`_\n- `More detailed example introducing the features of PyTorchSpiking\n  \u003chttps://www.nengo.ai/pytorch-spiking/examples/spiking-fashion-mnist.html\u003e`_\n- `API reference \u003chttps://www.nengo.ai/pytorch-spiking/reference.html\u003e`_\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnengo%2Fpytorch-spiking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnengo%2Fpytorch-spiking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnengo%2Fpytorch-spiking/lists"}