Datasetpublic

OpenScientificCodeRegistry/Database

Open Scientific Code Registry (OSCR): the authors' scripts The code published by the authors of open-access neuroscience papers, as found and verified by Open Scientific Code Registry (OSCR). Each file is here exactly as it is at the source, at the verified commit, under the license of its repository. 421,275 unique files (3,782 MB of text) from 8,849 repositories, in 24 Parquet block(s). Only files whose repository's license allows redistribution, confirmed by the repository's… See the full description on the dataset page: https://huggingface.co/datasets/OpenScientificCodeRegistry/Database.

sourceHugging Faceotherupdated 2d agoView on Hugging Face

0likes5.6kdownloads

rootmanifests57github.com__engellab__latent_circuit_inference.json

github.com__engellab__latent_circuit_inference.json246 linesDownload Raw Back to 57

1{2 "format": "oscr-script-manifest/1",3 "repository": "github.com/engellab/latent_circuit_inference",4 "url": "https://github.com/engellab/latent_circuit_inference",5 "host": "github.com",6 "commit": "7bd4a5773a32c6a2f31f4385ea4d290b268e6634",7 "license": "MIT",8 "license_confirmed_by": "license file LICENSE",9 "redistribution": "yes",10 "files": [11  {12   "path": "LICENSE",13   "sha256": "ec4ce6274931f07a0f57478d19d9319247f7872d00ffd71b70a2a9a296d6f229",14   "language": "License",15   "lines": 21,16   "truncated": false,17   "block": 17,18   "row": 766819  },20  {21   "path": "README.md",22   "sha256": "ca47d64eeb6f98f215edb1f90ed441ec0e60c620ce091c0cec6bc42e85f29257",23   "language": "Text",24   "lines": 25,25   "truncated": false,26   "block": 18,27   "row": 1054128  },29  {30   "path": "jupyter/Inferring latent circuit from CDDM RNN.ipynb",31   "sha256": "d13ae60f7ac06c6edabc1873971bc53cecc8ff64567d0300de81043655f6f0a3",32   "language": "Jupyter",33   "lines": 338,34   "truncated": false,35   "block": 17,36   "row": 677537  },38  {39   "path": "jupyter/Inferring latent circuit from Tanh networks.ipynb",40   "sha256": "b9761ad5139276a9e6671d581aa139e224873a7718bf06daffde99da165eee42",41   "language": "Jupyter",42   "lines": 346,43   "truncated": false,44   "block": 17,45   "row": 675146  },47  {48   "path": "jupyter/Inferring latent circuit with initial condition informed by clustering.ipynb",49   "sha256": "6fe26bee512f13ca4fc56c1dd0d01601a25c87ab55a20f7a0189f7520393c294",50   "language": "Jupyter",51   "lines": 409,52   "truncated": false,53   "block": 17,54   "row": 698455  },56  {57   "path": "jupyter/Perturbation of networks weights.ipynb",58   "sha256": "7ba264492435acf44e173142becfcc718b24257bf4aac39829057c8f6d2b67f2",59   "language": "Jupyter",60   "lines": 404,61   "truncated": false,62   "block": 17,63   "row": 696464  },65  {66   "path": "jupyter/pruning the RNN.ipynb",67   "sha256": "9648873749217f2b51c83674c92cb3c5380ed3347f4c8e61d647d06330dc9ceb",68   "language": "Jupyter",69   "lines": 344,70   "truncated": false,71   "block": 17,72   "row": 675073  },74  {75   "path": "latent_circuit_inference/LCAnalyzer.py",76   "sha256": "caebb2ae73c48df08f832e11ac93b13cc5b06b0c63710b72b27eb571713ef2b0",77   "language": "Python",78   "lines": 172,79   "truncated": false,80   "block": 17,81   "row": 3193982  },83  {84   "path": "latent_circuit_inference/LatentCircuit.py",85   "sha256": "a492ec6e247eefc6cd3a4b0f626ee2ac1c05edea3f2c8817394847ad3b419985",86   "language": "Python",87   "lines": 167,88   "truncated": false,89   "block": 17,90   "row": 3126091  },92  {93   "path": "latent_circuit_inference/LatentCircuitFitter.py",94   "sha256": "1fe7b1c4466ecd8e9f0fbd34d3508bbcae3bab0677613047b597780587feb23b",95   "language": "Python",96   "lines": 235,97   "truncated": false,98   "block": 18,99   "row": 812100  },101  {102   "path": "latent_circuit_inference/experimental/calculating_means.py",103   "sha256": "c6cec7fac98699cb70dbaaee7a803469e563862101c3fe058848c94f5e334de1",104   "language": "Python",105   "lines": 335,106   "truncated": false,107   "block": 18,108   "row": 3640109  },110  {111   "path": "latent_circuit_inference/experimental/manipulating_selection_vectors.py",112   "sha256": "6871c7f8d4f07ab42d748b21eeed862b7192401e111f15d2cc688050a64b8f75",113   "language": "Python",114   "lines": 260,115   "truncated": false,116   "block": 18,117   "row": 1920118  },119  {120   "path": "latent_circuit_inference/experimental/replot_connectivity_matrices.py",121   "sha256": "bc22981cf5cd716a4b58e4e96a09f6e4ace47b34d58f3f97371fb8490c5b2a84",122   "language": "Python",123   "lines": 41,124   "truncated": false,125   "block": 17,126   "row": 22106127  },128  {129   "path": "latent_circuit_inference/run_inference/run_LCI.py",130   "sha256": "a85cd42ec5d7546aca89162e53e1164f5960a782f45ba9964b028f9b8ef6d4e4",131   "language": "Python",132   "lines": 231,133   "truncated": false,134   "block": 18,135   "row": 560136  },137  {138   "path": "latent_circuit_inference/run_inference/run_LCI_BlockDMtanh.py",139   "sha256": "a9acb18549e310ffdc27594061d123565b4dc07e3c2b1026a5bacd46a9554e89",140   "language": "Python",141   "lines": 266,142   "truncated": false,143   "block": 18,144   "row": 1379145  },146  {147   "path": "latent_circuit_inference/run_inference/run_LCI_CDDMrelu.py",148   "sha256": "4c485866aa6b7dfd9664dbe179148b7f0936caf41484767c14fe464ba5a75cb8",149   "language": "Python",150   "lines": 225,151   "truncated": false,152   "block": 18,153   "row": 542154  },155  {156   "path": "latent_circuit_inference/run_inference/run_LCI_CDDMtanh.py",157   "sha256": "4f7793f6994a2f4776897b3ebd8d29746f0db39b213e253142bc3e2eff413ed9",158   "language": "Python",159   "lines": 275,160   "truncated": false,161   "block": 18,162   "row": 1233163  },164  {165   "path": "latent_circuit_inference/run_inference/run_LCI_ColorDiscrimination.py",166   "sha256": "b2222a94625876f23bba49291e37bba317f6bdcb009f8210d32f4330210fee74",167   "language": "Python",168   "lines": 280,169   "truncated": false,170   "block": 18,171   "row": 1518172  },173  {174   "path": "latent_circuit_inference/run_inference/run_LCI_DMTS.py",175   "sha256": "c25136e9944b96e34a92c295427eb9cd066a8a004e6e99a87613edf3e3961611",176   "language": "Python",177   "lines": 273,178   "truncated": false,179   "block": 18,180   "row": 1269181  },182  {183   "path": "latent_circuit_inference/run_inference/run_LCI_Mante_nets.py",184   "sha256": "436b4e68c8eea7c2339de1a702ed86f4411d57410395dce2689d065da00dab78",185   "language": "Python",186   "lines": 269,187   "truncated": false,188   "block": 18,189   "row": 1430190  },191  {192   "path": "latent_circuit_inference/run_inference/run_LCI_MemoryDM.py",193   "sha256": "9fc281cd96ab1dafbebe54158a8380b16c27f35c9b406e09efea975a1e811450",194   "language": "Python",195   "lines": 219,196   "truncated": false,197   "block": 17,198   "row": 32815199  },200  {201   "path": "latent_circuit_inference/run_inference/run_LCI_locally.py",202   "sha256": "5ffcb39748b44623d23e3c9503201223fcf60a2e99a03a9cc4d777aa0be34308",203   "language": "Python",204   "lines": 304,205   "truncated": false,206   "block": 18,207   "row": 2722208  },209  {210   "path": "latent_circuit_inference/run_inference/run_LCI_locally_CDDMplus.py",211   "sha256": "a56b6833e02b53016c9368f7d9c912b6457b8bb8c410499f9315ec0974530003",212   "language": "Python",213   "lines": 273,214   "truncated": false,215   "block": 18,216   "row": 1940217  },218  {219   "path": "latent_circuit_inference/utils/plotting_functions.py",220   "sha256": "6c2f1554aa1a458771aa6a4eb7fa704e2759794bc992e63dc7898e73cc722301",221   "language": "Python",222   "lines": 136,223   "truncated": false,224   "block": 17,225   "row": 29052226  },227  {228   "path": "latent_circuit_inference/utils/utils.py",229   "sha256": "9ff4b2d46f3d6bcb68e742478d0934ee76fc8394cf1b4aa8fd68345f8809ea88",230   "language": "Python",231   "lines": 127,232   "truncated": false,233   "block": 17,234   "row": 26294235  },236  {237   "path": "setup.py",238   "sha256": "35c330ac1dae3683ddda6d8bde030b152a88bee693ac7f97807046091a1e1bdc",239   "language": "Python",240   "lines": 11,241   "truncated": false,242   "block": 17,243   "row": 18218244  }245 ]246}