METHOD AND APPARATUS FOR EVENT-BASED LEARNING IN NEURAL NETWORK

摘要

A machine-learning method is provided, in which a restricted Boltzmann Machine (RBM) is modified to be effectively operate in an online environment by training a learning parameter asynchronously based on spiking events which occurs only when a spiking timing of a visual layer and a spiking timing of a hidden layer are within predetermined time window.

主权项

1. A method for a machine-learning apparatus to train a learning parameter for simulating a neural network, the method comprising:
activating a first visible layer based on input data; activating a first hidden layer based on a first learning parameter of the first visible layer and the first hidden layer and an output of the first visible layer; training the first learning parameter based on the output of the first visible layer and an output of the first hidden layer; activating a second visible layer corresponding to an auxiliary layer of the first visible layer based on the output of the first hidden layer; activating a second hidden layer corresponding to an auxiliary layer of the first hidden layer based on a second learning parameter of the second visible layer and the second hidden layer and an output of the second visible layer; and training the second learning parameter based on the output of the second visible layer and an output of the second hidden layer.