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Event-Driven CD in Spiking Neural Networks
This project forked from eneftci/ecd
Event-Driven CD in Spiking Neural Networks
#----------------------------------------------------------------------------- # File Name : README.py # Purpose: README file # # Author: Emre Neftci # # Creation Date : 27-06-2014 # Last Modified : # # Copyright : (c) UCSD, Emre Neftci, Srinjoy Das, Bruno Pedroni, Kenneth Kreutz-Delgado, Gert Cauwenberghs # Licence : GPLv2 #----------------------------------------------------------------------------- Python pre-requisites --------------------- -Brian (http://briansimulator.org/) -Numpy (http://www.numpy.org/) -Matplotlib (http://matplotlib.org/) For kldivergence.py only: -neuro_kl (https://github.com/pberkes/neuro-kl) Other pre-requisites: --------------------- - git (http://git-scm.com/) Installation steps ------------------ >>> git clone http://github.com/eneftci/eCD Running the scripts ------------------- The scripts in the experiments directory generate the data necessary for the figures in the paper "Event-driven Constrastive Divergence for Spiking Neuromorphic Systems". calibrate.py: Calibrates the parameters of the sigmoid function by fitting the parameters gamma and beta to the transfer function of the IF neuron (Fig. 2) accuracy_pre_trained.py Tests recognition accuracy on the entire MNIST test-set accuracy_pre_trained_finite.py Same as above but discretizes parameter to finite precision. train_mnist.py Main script for training the RBM using eCD convergence.py Generates (panels for Fig. 7) from a pre-trained RBM showing the convergence of the eCD rule. demonstrate_generative_model.py Generates data for Fig. 9 The following files run the RBM + eCD (CD) on MNIST data, they can be ran directly, but are primarily intended to be used through the previously described scripts. MNIST_IF_STDP.py: Script containing the main functions for training the Integrate and Fire neuron based RBM using event-driven CD MNIST_IF_STDP_SEQ.py: Same as above, but with modification for plotting purposes in demonstrate_generative_model.py MNIST_IF_STDP_WMON.py: Same as above, but with modification for plotting purposes in convergence.py MNIST_SRM_STDP.py: Similar to above but using the ideal transfer curve (i.e. the Spike Response Model) MNIST_SRM_RATE.py: Similar to above, but using standard CD and the ideal transfer curve (i.e. the Spike Response Model) parameters_*.py: These files contain parameters used for the experiments scripts File permissions convention --------------------------- Files that are designed to run standalone (with a if __name__ == '__main__' statement) have executable permissions, the others do not. Remarks about customization --------------------------- The neuron models are specified in neusa/experimentLib.py
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