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    "info": {
        "author": "Saranraj Nambusubramaniyan",
        "author_email": "saran_nns@hotmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "## Self-Organizing Recurrent Neural Networks \n\nSORN is a class of neuro-inspired artificial network build based on plasticity mechanisms in biological brain and mimic neocortical circuits ability of learning and adaptation through neuroplasticity mechanisms.\n\nFor ease of maintanance, example use cases and the API(under developement) are moved to https://github.com/Saran-nns/PySORN_0.1 \n\n[![Build Status](https://travis-ci.org/Saran-nns/sorn.svg?branch=master)](https://travis-ci.org/Saran-nns/sorn)\n[![codecov](https://codecov.io/gh/Saran-nns/sorn/branch/master/graph/badge.svg)](https://codecov.io/gh/Saran-nns/sorn)\n[![Join the chat at https://gitter.im/Self-Organizing-Recurrent-Neural-Networks](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/Self-Organizing-Recurrent-Neural-Networks?utm_source=badge&utm_medium=badge&utm_content=badge)\n[![PyPI version](https://badge.fury.io/py/sorn.svg)](https://badge.fury.io/py/sorn)\n![PyPI - Downloads](https://img.shields.io/pypi/dw/sorn.svg)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2593681.svg)](https://doi.org/10.5281/zenodo.2593681)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\n#### To install the latest release:\n\n```python\npip install sorn\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/sorn\n```\n\n#### Dependencies\nSORN supports Python 3.5+ ONLY. For older Python versions please use the official Python client\n\n\n#### Usage:\n\n##### Update Network configurations\n\nNavigate to home/conda/envs/ENVNAME/Lib/site-packages/sorn\n\nor if you are unsure about the directory of sorn\n\nRun\n\n```python\nimport sorn\n\nsorn.__file__\n```\nto find the location of the sorn package\n\nThen, update/edit the configuration.ini\n\n\n##### Plasticity Phase\n\n```Python\n# Import \nfrom sorn.sorn import RunSorn\n\n# Sample input \ninputs = [0.]\n\n# To simulate the network; \nmatrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = RunSorn(phase='Plasticity', matrices=None,\n                                                                          time_steps=100).run_sorn(inputs)\n\n# To resume the simulation, load the matrices_dict from previous simulation;\nmatrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = RunSorn(phase='Plasticity', matrices=matrices_dict,\n                                                                          time_steps=100).run_sorn(inputs)\n```\n\n##### Training phase:\n\n```Python\nmatrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = RunSorn(phase='Training', matrices=matrices_dict,\n                                                                          time_steps=100).run_sorn(inputs)\n```\n\n#### Network Output Descriptions:\n    matrices_dict  - Dictionary of connection weights ('Wee','Wei','Wie') , Excitatory network activity ('X'), Inhibitory network activities('Y'), Threshold values ('Te','Ti')\n\n    Exc_activity - Collection of Excitatory network activity of entire simulation period\n\n    Inh_activitsy - Collection of Inhibitory network activity of entire simulation period\n\n    Rec_activity - Collection of Recurrent network activity of entire simulation period\n\n    num_active_connections - List of number of active connections in the Excitatory pool at each time step \n\n\n#### Sample Plotting functions \n\n\n\n```Python\nfrom sorn.utils import Plotter\n# Plot weight distribution in the network\nPlotter.weight_distribution(weights= matrices_dict['Wee'], bin_size = 5, savefig = False)\n\n# Plot Spike train of all neurons in the network\nPlotter.scatter_plot(spike_train = np.asarray(Exc_activity), savefig=False)\n\n\nPlotter.raster_plot(spike_train = np.asarray(Exc_activity), savefig=False)\n```\n\n#### Sample Statistical analysis functions\n\n```Python\nfrom sorn.utils import Statistics\n#t-lagged auto correlation between neural activity\nStatistics.autocorr(firing_rates = [1,1,5,6,3,7],t= 2)\n\n# Fano factor: To verify poissonian process in spike generation of neuron 10\nStatistics.fanofactor(spike_train= np.asarray(Exc_activity),neuron = 10,window_size = 10)\n```\n\n#### Articles:\n\nLazar, A. (2009). SORN: a Self-organizing Recurrent Neural Network. Frontiers in Computational Neuroscience, 3. https://doi.org/10.3389/neuro.10.023.2009\n\nHartmann, C., Lazar, A., Nessler, B., & Triesch, J. (2015). Where\u00e2\u20ac\u2122s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network. PLoS Computational Biology, 11(12). https://doi.org/10.1371/journal.pcbi.1004640 \n\nDel Papa, B., Priesemann, V., & Triesch, J. (2017). Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network. PLoS ONE, 12(5). https://doi.org/10.1371/journal.pone.0178683 \n\nZheng, P., Dimitrakakis, C., & Triesch, J. (2013). Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex. PLoS Computational Biology, 9(1). https://doi.org/10.1371/journal.pcbi.1002848  \n\n#### Cite as:\n\nSaranraj Nambusubramaniyan. (2019, March 14). Saran-nns/sorn: sorn-alpha (Version v0.2.1). Zenodo. http://doi.org/10.5281/zenodo.2593681\n\n\n",
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        "keywords": "Brain-Inspired Computing,Artificial Neural Networks,Neuro Informatics,Spiking Cortical Networks,Neural Connectomics,Neuroscience,Artificial General Intelligence,Neural Information Processing",
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