{"id":25179649,"url":"https://github.com/saran-nns/rmsorn","last_synced_at":"2026-05-17T00:10:26.277Z","repository":{"id":37649167,"uuid":"271963530","full_name":"Saran-nns/rmsorn","owner":"Saran-nns","description":"PyPi package of Reward-Modulated Self Organizing Recurrent Neural Network","archived":false,"fork":false,"pushed_at":"2022-12-08T01:53:00.000Z","size":411,"stargazers_count":1,"open_issues_count":2,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-09T15:38:50.264Z","etag":null,"topics":["artifical-intelligense","brain-inspired-computing","criticality","deep-learning","dynamical-modeling","machine-learning","neuroscience","reinforcement-learning","self-organizing-network","spiking-cortical-model","spiking-neural-networks"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Saran-nns.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-06-13T07:41:11.000Z","updated_at":"2021-10-21T22:18:20.000Z","dependencies_parsed_at":"2023-01-25T02:15:39.179Z","dependency_job_id":null,"html_url":"https://github.com/Saran-nns/rmsorn","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/Saran-nns%2Frmsorn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saran-nns%2Frmsorn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saran-nns%2Frmsorn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saran-nns%2Frmsorn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Saran-nns","download_url":"https://codeload.github.com/Saran-nns/rmsorn/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247117762,"owners_count":20886439,"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":["artifical-intelligense","brain-inspired-computing","criticality","deep-learning","dynamical-modeling","machine-learning","neuroscience","reinforcement-learning","self-organizing-network","spiking-cortical-model","spiking-neural-networks"],"created_at":"2025-02-09T15:37:38.133Z","updated_at":"2025-10-07T12:39:02.753Z","avatar_url":"https://github.com/Saran-nns.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Reward Modulated Self-Organizing Recurrent Neural Networks \n\nPyPi package of RM-SORN: a reward-modulated self-organizing recurrent neural network: [RMSORN-Paper](https://doi.org/10.3389/fncom.2015.00036)\n\n[![Build Status](https://travis-ci.org/Saran-nns/rmsorn.svg?branch=master)](https://travis-ci.org/Saran-nns/rmsorn)\n[![codecov](https://codecov.io/gh/Saran-nns/rmsorn/branch/master/graph/badge.svg)](https://codecov.io/gh/Saran-nns/rmsorn)\n[![PyPI version](https://badge.fury.io/py/rmsorn.svg)](https://badge.fury.io/py/rmsorn)\n[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/github/license/Saran-nns/rmsorn)\n\n#### To install the latest release:\n\n```python\npip install rmsorn\n```\n\nThe library is still in alpha stage, so you may also want to install the latest version from the development branch:\n\n```python\npip install git+https://github.com/Saran-nns/rmsorn\n```\n#### Usage:\n##### Update Network configurations\n\nNavigate to home/conda/envs/ENVNAME/Lib/site-packages/rmsorn\n\nor if you are unsure about the directory of rmsorn\n\nRun\n\n```python\nimport rmsorn\n\nrmsorn.__file__\n```\nto find the location of the rmsorn package\n\nThen, update/edit the configuration.ini\n\n```python\nfrom rmsorn.tasks import PatternRecognition\n\ninputs, targets = PatternRecognitionTask.generate_sequence()\ntrain_plast_inp_mat,X_all_inp,Y_all_inp,R_all, frac_pos_active_conn = SimulateRMSorn(phase = 'Plasticity', \n                                                                                      matrices = None,\n                                                                                      inputs = np.asarray(inputs),sequence_length = 4, targets = targets,\n                                                                                      reward_window_sizes = [1,5,10,20],\n                                                                                      epochs = 1).train_rmsorn()\n```\n\nNotebook is avaialble at [RMSORN-Notebook](https://github.com/Saran-nns/PySORN_0.1/blob/master/v0.1.0/notebooks/alpha_cpu/RMSORN_pattern_recognition.ipynb)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaran-nns%2Frmsorn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaran-nns%2Frmsorn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaran-nns%2Frmsorn/lists"}