{"id":47336650,"url":"https://github.com/structuralneurobiologylab/syconn","last_synced_at":"2026-03-17T22:00:34.637Z","repository":{"id":71343371,"uuid":"50827402","full_name":"StructuralNeurobiologyLab/SyConn","owner":"StructuralNeurobiologyLab","description":"Toolkit for the generation and analysis of volume eletron microscopy based synaptic connectomes of brain tissue.","archived":false,"fork":false,"pushed_at":"2025-07-28T13:01:34.000Z","size":268489,"stargazers_count":41,"open_issues_count":6,"forks_count":9,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-07-28T15:05:26.586Z","etag":null,"topics":["connectivity-matrix","connectomics","image-analysis","image-processing","morphology-analysis","semantic-segmentation"],"latest_commit_sha":null,"homepage":"http://structuralneurobiologylab.github.io/SyConn/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/StructuralNeurobiologyLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2016-02-01T09:02:30.000Z","updated_at":"2025-02-28T14:05:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"7b5e2b51-5a2f-4825-bd23-aebdb3f62c1a","html_url":"https://github.com/StructuralNeurobiologyLab/SyConn","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/StructuralNeurobiologyLab/SyConn","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/StructuralNeurobiologyLab%2FSyConn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/StructuralNeurobiologyLab%2FSyConn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/StructuralNeurobiologyLab%2FSyConn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/StructuralNeurobiologyLab%2FSyConn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/StructuralNeurobiologyLab","download_url":"https://codeload.github.com/StructuralNeurobiologyLab/SyConn/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/StructuralNeurobiologyLab%2FSyConn/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30633125,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-17T17:32:55.572Z","status":"ssl_error","status_checked_at":"2026-03-17T17:32:38.732Z","response_time":56,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["connectivity-matrix","connectomics","image-analysis","image-processing","morphology-analysis","semantic-segmentation"],"created_at":"2026-03-17T22:00:10.518Z","updated_at":"2026-03-17T22:00:34.630Z","avatar_url":"https://github.com/StructuralNeurobiologyLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg align=\"right\" width=\"300\" src=\"./docs/_static/logo_SyConn.png\"\u003e\u003cbr/\u003e\n[![Documentation Status](https://readthedocs.org/projects/syconn/badge/?version=latest)](https://syconn.readthedocs.io/en/latest/?badge=latest)\n[![pipeline status](https://gitlab.mpcdf.mpg.de/pschuber/SyConn/badges/master/pipeline.svg)](https://gitlab.mpcdf.mpg.de/pschuber/SyConn/commits/master)\n[![pylint status](https://gitlab.mpcdf.mpg.de/pschuber/SyConn/-/jobs/artifacts/master/raw/pylint/pylint.svg?job=pylint)](https://gitlab.mpcdf.mpg.de/pschuber/SyConn/-/jobs/artifacts/master/raw/pylint/pylint.log?job=pylint)\n\nSyConn2\n------\nConnectomic analysis toolkit for automated synaptic connectivity inference based on dense EM segmentation data.\n\nCurrent features:\n- interface to supervoxel properties for various types (e.g. cell fragments, synapses, mitochondria, vesicle clouds, ..) and entire cell reconstructions \n(associated cell objects, mesh, skeleton, prediction results, ..)\n- (sub-) cellular compartment (spines, boutons and axon/dendrite/soma) and cell type classification with multiview- [\\[2\\]](https://www.nature.com/articles/s41467-019-10836-3) and with skeleton-based approaches [\\[1\\]](https://www.nature.com/articles/nmeth.4206)\n- distributed parallelization (SLURM, QSUB) of all processing steps\n- astrocyte identification and separation [\\[2\\]](https://www.nature.com/articles/s41467-019-10836-3)\n- neuroglancer web interface\n\nIf you use parts of this code base in your academic projects, please cite the corresponding publication(s).\n\nTo access v1 of SyConn \nplease checkout the branch [dorkenwald2017nm](https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm) or have a look at \nthe [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/). \nWe also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).\n\nDocumentation\n-------------\nThe documentation including installation procedure, requirements and API docs can be found as [readthedocs build](https://syconn.readthedocs.io/en/latest/) or partially as markdown [here](docs/doc.md).\n\nThe Team\n--------\nThe Synaptic connectivity inference toolkit is currently developed at the Max-Planck-Institute of Neurobiology in Martinsried by\n Philipp Schubert, Jonathan Klimesch, Alexandra Rother and Joergen Kornfeld.\nBig thanks to Filippo Kiessler, David Outland, Santiago Aguirre, Hashir Ahmad, Andrei Mancu, Rangoli Saxena, Mariana Shumliakivska,\nJosef Mark, Maria Kawula, Atul Mohite, Carl Constantin v. Wedemeyer,\nGaurav Kumar and Martin Drawitsch for code contributions.\n\nAcknowledgements\n----------------\nWe are especially grateful for the support by Winfried Denk who enabled\nthis work in his department. We also want to thank Christian\nGuggenberger and his group at the MPCDF for cluster support and deepmind\nfor providing egl extension code to handle multi-gpu rendering on the\nsame machine. The original code snippet (under the Apache License 2.0)\nused for our project can be found\n[here](https://github.com/deepmind/dm_control/blob/30069ac11b60ee71acbd9159547d0bc334d63281/dm_control/_render/pyopengl/egl_ext.py).\nSyConn uses the packages [zmesh](https://github.com/seung-lab/zmesh) for mesh and [kimimaro](https://github.com/seung-lab/kimimaro)\nfor skeleton generation implemented and developed in the Seung Lab.\nThanks to Julia Kuhl (see http://somedonkey.com/ for more beautiful\nwork) for designing and creating the logo!\n\n\nPublications\n------------\n\\[1\\] [Automated synaptic connectivity inference for volume electron microscopy](https://www.nature.com/articles/nmeth.4206)\n```\n @ARTICLE{SyConn2017,\n   title     = \"Automated synaptic connectivity inference for volume electron\n                microscopy\",\n   author    = \"Dorkenwald, Sven and Schubert, Philipp J and Killinger, Marius F\n                and Urban, Gregor and Mikula, Shawn and Svara, Fabian and\n                Kornfeld, Joergen\",\n   abstract  = \"SyConn is a computational framework that infers the synaptic\n                wiring of neurons in volume electron microscopy data sets with\n                machine learning. It has been applied to zebra finch, mouse and\n                zebrafish neuronal tissue samples.\",\n   journal   = \"Nat. Methods\",\n   publisher = \"Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.\",\n   year      = 2017,\n   month     = Feb,\n   day       = 27,\n   url       = http://dx.doi.org/10.1038/nmeth.4206\n }\n  ```\n\n\\[2\\] [Learning cellular morphology with neural networks](https://doi.org/10.1038/s41467-019-10836-3)\n  ```\n  @Article{Schubert2019,\nauthor={Schubert, Philipp J.\nand Dorkenwald, Sven\nand Januszewski, Michal\nand Jain, Viren\nand Kornfeld, Joergen},\ntitle={Learning cellular morphology with neural networks},\njournal={Nature Communications},\nyear={2019},\nvolume={10},\nnumber={1},\npages={2736},\nabstract={Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but\ncan reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated\nneuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological\nanalysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications.\nHere, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically\nreconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings\n(Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm,\nwhich are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify\nsubcellular compartments and the cell types of neuron reconstructions.},\nissn={2041-1723},\ndoi={10.1038/s41467-019-10836-3},\nurl={https://doi.org/10.1038/s41467-019-10836-3}\n}\n  ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstructuralneurobiologylab%2Fsyconn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstructuralneurobiologylab%2Fsyconn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstructuralneurobiologylab%2Fsyconn/lists"}