{"id":21684628,"url":"https://github.com/nengo/nengo-gyrus","last_synced_at":"2026-05-18T17:41:00.748Z","repository":{"id":54520402,"uuid":"327935565","full_name":"nengo/nengo-gyrus","owner":"nengo","description":"Recursively generate large-scale Nengo models using NumPy semantics.","archived":false,"fork":false,"pushed_at":"2021-02-13T11:36:52.000Z","size":307,"stargazers_count":3,"open_issues_count":2,"forks_count":0,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-01-25T11:44:11.023Z","etag":null,"topics":["functional-programming","nengo","neural-networks","numpy","python"],"latest_commit_sha":null,"homepage":"","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}},"created_at":"2021-01-08T15:09:29.000Z","updated_at":"2023-11-07T21:14:42.000Z","dependencies_parsed_at":"2022-08-13T18:30:57.677Z","dependency_job_id":null,"html_url":"https://github.com/nengo/nengo-gyrus","commit_stats":null,"previous_names":["nengo/nengo-gyrus"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fnengo-gyrus","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fnengo-gyrus/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fnengo-gyrus/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nengo%2Fnengo-gyrus/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nengo","download_url":"https://codeload.github.com/nengo/nengo-gyrus/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244602711,"owners_count":20479676,"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":["functional-programming","nengo","neural-networks","numpy","python"],"created_at":"2024-11-25T16:16:31.495Z","updated_at":"2026-05-18T17:40:55.685Z","avatar_url":"https://github.com/nengo.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":".. image:: https://i.imgur.com/xef9dhv.png\n  :width: 100%\n  :target: https://github.com/nengo-labs/nengo-gyrus\n  :alt: Recursively generate large-scale Nengo models using NumPy semantics.\n\nRecursively generate large-scale `Nengo \u003chttp://nengo.ai/\u003e`_ models using\n`NumPy \u003chttps://numpy.org/\u003e`_ semantics.\n\nQuick Start\n===========\n\n.. code-block:: bash\n\n   pip install git+https://github.com/nengo-labs/nengo-gyrus\n\nAn example of computing the square of a two-dimensional vector with Gyrus:\n\n.. code-block:: python\n\n  import gyrus\n  import matplotlib.pyplot as plt\n  import numpy as np\n\n  u = gyrus.stimuli([np.cos, np.sin])\n  x = (u ** 2).filter(0.01)\n  y = np.asarray(x.run(np.pi))  # shape: (fold, time, size_out)\n\n  plt.figure()\n  plt.plot(y.squeeze(axis=-1).T)\n  plt.xlabel(\"Time-step\")\n  plt.show()\n\n.. image:: https://i.imgur.com/KyDKeyc.png\n  :width: 100%\n  :target: https://github.com/nengo-labs/nengo-gyrus\n  :alt: Computing the square of a two-dimensional vector with Gyrus.\n\nThis code is automagically converted to `Nengo \u003chttp://nengo.ai/\u003e`_ and implemented\nvia two spiking LIF ensembles and a lowpass synapse.\n\nGyrus supports many common NumPy 'ufuncs', array functions, and numeric Python\noperators. Thus, code can be written in a functional style using N-D arrays and then\nrealized as a Nengo neural network. This enables algorithms to be written in NumPy and\nthen compiled onto `Nengo's supported backends \u003chttps://www.nengo.ai/documentation/\u003e`_\n(e.g., GPUs, microcontrollers, neuromorphic hardware, and other neural network\naccelerators).\n\nDocumentation\n=============\n\nCheck out and render the Jupyter notebooks located in\n`docs/examples \u003chttps://github.com/nengo-labs/nengo-gyrus/tree/master/docs/examples\u003e`_.\n\nThe `gyrus_overview\n\u003chttps://github.com/nengo-labs/nengo-gyrus/blob/master/docs/examples/gyrus_overview.ipynb\u003e`_\nnotebook is currently the best starting point to learn the Gyrus API and see a variety\nof examples.\n\nSupport\n=======\n\nTested against ``nengo\u003e=3.0.0`` and requires ``numpy\u003e=1.17``.\n\nThis project is currently pre-alpha. Pull requests are welcome, as are breaking (i.e.,\nreverse-incompatible) changes.\n\nIf something doesn't work quite as you thought it should, or if you have ideas for\nimprovements, please feel free to open up a `GitHub issue\n\u003chttps://github.com/nengo-labs/nengo-gyrus/issues\u003e`_ or post on the `Nengo Forum\n\u003chttps://forum.nengo.ai/\u003e`_.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnengo%2Fnengo-gyrus","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnengo%2Fnengo-gyrus","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnengo%2Fnengo-gyrus/lists"}