sorn 0.0.5

Self-Organizing Recurrent Neural Networks

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:

pip install sorn

The library is still in alpha, so you may also want to install the latest version from the development branch:

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/

Usage:

Plasticity Phase

# 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:

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