Metadata-Version: 2.1
Name: sorn
Version: 0.1.3
Summary: Self-Organizing Recurrent Neural Networks
Home-page: https://github.com/Saran-nns/sorn
Author: Saranraj Nambusubramaniyan
Author-email: saran_nns@hotmail.com
License: BSD
Description: ## Self-Organizing Recurrent Neural Networks 
        
        SORN is a class of neuro-inspired computing model build based on plasticity mechanisms in biological brain and mimic neocortical circuits ability of learning and adaptation through neuroplasticity mechanisms.
        
        #### To install the latest release:
        
        ```python
        pip install sorn
        ```
        
        The library is still in alpha, so you may also want to install the latest version from the development branch:
        
        ```python
        pip install git+https://github.com/Saran-nns/sorn
        ```
        
        #### Dependencies
        SORN supports Python 3.5+ ONLY. For older Python versions please use the official Python client
        
        
        #### Check and Update Network configurations
        
        Navigate to c:/Users/USERNAME/AppData/Local/conda/conda/envs/ENVNAME/Lib/site-packages/sorn
        
        Then, update/edit the configuration of your interest
        
        #### Usage: 
        
        ##### Plasticity Phase
        
        ```Python
        # Import 
        from sorn.sorn import RunSorn
        
        # Sample input 
        inputs = [0.]
        
        # To simulate the network; 
        matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = RunSorn(phase='Plasticity', matrices=None,
                                                                                  time_steps=100).run_sorn(inputs)
        
        # To resume the simulation, load the matrices_dict from previous simulation;
        matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = RunSorn(phase='Plasticity', matrices=matrices_dict,
                                                                                  time_steps=100).run_sorn(inputs)
        ```
        
        ##### Training phase:
        
        ```Python
        matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = RunSorn(phase='Training', matrices=matrices_dict,
                                                                                  time_steps=100).run_sorn(inputs)
        ```
        
        #### Network Output Descriptions:
            matrices_dict  - Dictionary of connection weights ('Wee','Wei','Wie') , Excitatory network activity ('X'), Inhibitory network activities('Y'), Threshold values ('Te','Ti')
        
            Exc_activity - Collection of Excitatory network activity of entire simulation period
        
            Inh_activitsy - Collection of Inhibitory network activity of entire simulation period
        
            Rec_activity - Collection of Recurrent network activity of entire simulation period
        
            num_active_connections - List of number of active connections in the Excitatory pool at each time step 
        
Keywords: Brain-Inspired Computing,Artificial Neural Networks,Neuro Informatics,Spiking Cortical Networks,Neural Connectomics,Neuroscience,Artificial General Intelligence,Neural Information Processing
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
