sorn 0.3.12

Self-Organizing Recurrent Neural Networks

Self-Organizing Recurrent Neural Networks

SORN is a class of neuro-inspired artificial network build based on plasticity mechanisms in biological brain and mimic neocortical circuits ability of learning and adaptation through neuroplasticity mechanisms.

The network is developed as part of my Master thesis at Universität Osnabrück, Germany. For the ease of maintainance, the notebooks, 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

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

There are two ways to update/configure the network parameters,

1. 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 arguments in configuration.ini

2. Pass the arguments with valid names (listed below). This will override the default values at configuration.ini . The allowed kwargs are,

kwargs_ = ['ne', 'nu', 'network_type_ee', 'network_type_ei', 'network_type_ie', 'lambda_ee','lambda_ei', 'lambda_ie', 'eta_stdp','eta_inhib', 'eta_ip', 'te_max', 'ti_max', 'ti_min', 'te_min', 'mu_ip','sigma_ip']

Simulation: Plasticity Phase

The default ne, nu values are overriden by passing them as kwargs insidesimulate_sorn method.

# Import 
from sorn import Simulator

# Sample input 
num_features = 10
time_steps = 200
inputs = np.random.rand(num_features,timesteps)

# To simulate the network; 
matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = Simulator.simulate_sorn(inputs = inputs, phase='Plasticity', matrices=None, noise = True, time_steps=time_steps, ne = 200, nu=num_features)

# To resume the simulation, load the matrices_dict from previous simulation;
matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = Simulator.simulate_sorn(inputs = inputs, phase='Plasticity', matrices=matrices_dict, noise= True, time_steps=time_steps,ne = 200, nu=num_features)

Training phase:

from sorn import Trainer
inputs = np.random.rand(num_features,1) 

# SORN network is frozen during training phase
matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = Trainer.train_sorn(inputs = inputs, phase='Training', matrices=matrices_dict,nu=num_features, time_steps=1)

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_activity - 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 use with OpenAI gym :

Cartpole balance problem

Without changing the default network parameters.

# Imports

from sorn import Simulator, Trainer
import gym

# Load the simulated network matrices
# Note these matrices are obtained after the network achieved convergence under random inputs and noise

with open('simulation_matrices.pkl','rb') as f:  
    sim_matrices,excit_states,inhib_states,recur_states,num_reservoir_conn = pickle.load(f)

# Training parameters

NUM_EPISODES = 2e6
NUM_PLASTICITY_EPISODES = 20000

env = gym.make('CartPole-v0')

for EPISODE in range(NUM_EPISODES):

    # Environment observation
    state = env.reset()[None,:]

    # Play the episode
    while True:
      if EPISODE < NUM_PLASTICITY_EPISODE:

        # Plasticity phase
        sim_matrices,excit_states,inhib_states,recur_states,num_reservoir_conn = Simulator.simulate_sorn(inputs = state, phase ='Plasticity', matrices = sim_matrices, noise=False)

      else:
        # Training phase with frozen reservoir connectivity
        sim_matrices,excit_states,inhib_states,recur_states,num_reservoir_conn = Trainer.train_sorn(inputs = state, phase = 'Training', matrices = sim_matrices, noise= False)

      # Feed excit_states as input states to your RL algorithm, below goes for simple policy gradient algorithm
      # Sample policy w.r.t excitatory states and take action in the environment

      probs = policy(np.asarray(excit_states),output_layer_weights))
      action = np.random.choice(action_space,probs)
      state,reward,done,_ = env.step(action) 

      if done:
        break

Sample Plotting functions

from sorn 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 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)

Please cite the repo as,

@software{saranraj_nambusubramaniyan_2020_4184103,
  author       = {Saranraj Nambusubramaniyan},
  title        = {Saran-nns/sorn: Stable alpha release},
  month        = nov,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.3.1},
  doi          = {10.5281/zenodo.4184103},
  url          = {https://doi.org/10.5281/zenodo.4184103}
}

The network is inspired by following 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