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.
For ease of maintanance, example use cases and the API(under developement) are moved to https://github.com/Saran-nns/PySORN_0.1
SORN Reservoir and the evolution of synaptic efficacies
Neural Connectome
To install the latest release:
pip install sorn
The library is still in alpha stage, 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
Sample Plotting functions
from sorn.utils import Plotter
# Plot weight distribution in the network
Plotter.weight_distribution(weights= matrices_dict['Wee'], bin_size = 5, savefig = False)
# Plot Spike train of all neurons in the network
Plotter.scatter_plot(spike_train = np.asarray(Exc_activity), savefig=False)
Plotter.raster_plot(spike_train = np.asarray(Exc_activity), savefig=False)
Sample Statistical analysis functions
from sorn.utils import Statistics
#t-lagged auto correlation between neural activity
Statistics.autocorr(firing_rates = [1,1,5,6,3,7],t= 2)
# Fano factor: To verify poissonian process in spike generation of neuron 10
Statistics.fanofactor(spike_train= np.asarray(Exc_activity),neuron = 10,window_size = 10)
Articles:
Lazar, A. (2009). SORN: a Self-organizing Recurrent Neural Network. Frontiers in Computational Neuroscience, 3. https://doi.org/10.3389/neuro.10.023.2009
Hartmann, C., Lazar, A., Nessler, B., & Triesch, J. (2015). Where’s 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
Del 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
Zheng, 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
Citation:
Saranraj Nambusubramaniyan. (2019, March 11). Saran-nns/sorn: sorn alpha 0.1.5 (Version v0.1.5). Zenodo. http://doi.org/10.5281/zenodo.2590450
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