Self-Organizing Recurrent Neural Networks
Project 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:
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/Lib/site-packages/sorn
Then, update/edit the configuration of your interest
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
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