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 five fundamental neuroplasticity mechanisms.
Example use cases and the API(under developement) are maintained at https://github.com/Saran-nns/PySORN_0.1
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
Usage:
Update Network configurations
Navigate to home/conda/envs/ENVNAME/Lib/site-packages/sorn
or if you are unsure about the directory of sorn
Run
import sorn
sorn.__file__
to find the location of the sorn package
Then, update/edit the configuration.ini
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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