Method and apparatus for event-based learning in neural network

Patent metadata

Field	Value
Publication number	US-2016275397-A1
Application number	US-201514833264-A
Country	US
Kind code	A1
Filing date	Aug 24, 2015
Priority date	Mar 18, 2015
Publication date	Sep 22, 2016
Grant date	—

Abstract

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.

First claim

What is claimed is:

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.

2 . The method of claim 1 , wherein the first learning parameter represents a connection structure between the first visible layer and the first hidden layer, the second learning parameter represents a connection structure between the second visible layer and the second hidden layer, and the first learning parameter is identical to the second learning parameter.

3 . The method of claim 1 , wherein the training the first learning parameter comprises training the first learning parameter so that the output of the first hidden layer is similar to the input data, and
wherein the training the second learning parameter comprises training the second learning parameter so that the output of the second visible layer is similar to the output of the first visible layer.

4 . The method of claim 1 , wherein the first learning parameter comprises a first connection weight between a neuron comprised in the first visible layer and a neuron comprised in the first hidden layer, and
wherein the second learning parameter comprises a second connection weight between a neuron comprised in the second visible layer and a neuron comprised in the second hidden layer.

5 . The method of claim 1 , wherein neurons comprised in the first visible layer, the first hidden layer, the second visible layer, and the second hidden layer are activated based on a timing of a spike to be input.

6 . The method of claim 4 , wherein the first connection weight between a first visible neuron comprised in the first visible layer and a first hidden neuron comprised in the first hidden layer increases based on an output of the first visible neuron and an output of the first hidden neuron, and
the second connection weight between a second visible neuron comprised in the second visible layer and a second hidden neuron comprised in the second hidden layer decreases based on an output of the second visible neuron and an output of the second hidden neuron.

7 . The method of claim 6 , wherein the first connection weight between the first visible neuron and the first hidden neuron increases when a timing of a spike output by the first visible neuron and a timing of a spike output by the first hidden layer are within a predetermined time range.

8 . The method of claim 6 , wherein the second connection weight between the second visible neuron and the second hidden neuron decreases when a timing of a spike output by the second visible neuron and a timing of a spike output by the second hidden layer are within a predetermined time range.

9 . A computer program stored in a non-transitory computer-readable recording medium to implement the method of claim 1 .

10 . A learning apparatus, comprising:
a storage configured to store a computer program including instructions to simulate a neural network; and a processor configured to perform a method for training a learning parameter used in the neutral network by executing the computer program, 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.

11 . The learning apparatus of claim 10 , wherein the first learning parameter represents a connection structure between the first visible layer and the first hidden layer, the second learning parameter represents a connection structure between the second visible layer and the second hidden layer, and the first learning parameter is identical to the second learning parameter.

12 . The learning apparatus of claim 10 , wherein the first learning parameter is trained so that the output of the first hidden layer is similar to the input data and the second learning parameter is trained so that the output of the second visible layer is similar to the output of the first visible layer.

13 . The learning apparatus of claim 10 , wherein the first learning parameter comprises a first connection weight between a neuron comprised in the first visible layer and a neuron comprised in the hidden layer, and
wherein the second learning parameter comprises a second connection weight between a neuron comprised in the second visible layer and a neuron comprised in the second hidden layer.

14 . The learning apparatus of claim 10 , wherein neurons comprised in the first visible layer, the first hidden layer, the second visible layer, and the second hidden layer are activated based on a timing of a spike to be input.

15 . The learning apparatus of claim 13 , wherein the first connection weight between a first visible neuron comprised in the first visible layer and a first hidden neuron comprised in the first hidden layer increases based on an output of the first visible neuron and an output of the first hidden neuron, and
the second connection weight between a second visible neuron comprised in the second visible layer and a second hidden neuron comprised in the second hidden layer decreases based on an output of the second visible neuron and an output of the second hidden neuron.

16 . The learning apparatus of claim 15 , wherein the first connection weight between the first visible neuron and the first hidden neuron increases when a timing of a spike output by the first visible neuron and a timing of a spike output by the first hidden layer are within a predetermined time range.

17 . The learning apparatus of claim 15 , wherein the second connection weight between the second visible neuron and the second hidden neuron decreases when a timing of a spike output by the second visible neuron and a timing of a spike output by the second hidden layer are within a predetermined time range.

Assignees

• Samsung Electronics Co Ltd
• Univ Zuerich

Inventors

• Neil Daniel
• Pfeiffer Michael
• Liu Shih- Chii

Classifications

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title

• G06N3/0475

  Generative networks · CPC title

• G06N99/005

  Physics · mapped topic

• G06N3/10

  Interfaces, programming languages or software development kits, e.g. for simulating neural networks · CPC title