Self-adaptive threshold value neuron information processing method and system

An information processing method and information processing system technology, applied in the field of artificial neural network, to achieve the effect of reducing the difficulty of distribution, balancing the frequency of distribution, and improving the ability of information processing

Active Publication Date: 2017-06-20
TSINGHUA UNIV
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AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to provide an adaptive threshold neuron information processing method and system for how to effectively balance the firing frequency of each spike neuron in the entire neural network. The method includes:

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  • Self-adaptive threshold value neuron information processing method and system
  • Self-adaptive threshold value neuron information processing method and system
  • Self-adaptive threshold value neuron information processing method and system

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Embodiment Construction

[0095] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0096] figure 1 It is a schematic flow chart of an adaptive threshold neural network information processing method of an embodiment, such as figure 1 The shown adaptive threshold neural network information processing method includes:

[0097] Step S100, receiving the output information of the front-end spiking neuron.

[0098] Specifically, the output information of the front-end spike neuron is the pulse information output by the front-end spike neuron connected to the current spike neuron.

[0099] Step S200, read the current spike neuron information.

[0100] Specifically, the current spike ...

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Abstract

The invention relates to a self-adaptive threshold value neuron information processing method and system. The method comprises the steps that the front-end pulse neuron output information is received; the current pulse neuron information is read; the current pulse neuron output information is calculated based on the front-end pulse neuron output information and the current pulse neuron information; the current self-adaptive threshold value variable and threshold value potential are read, and the current self-adaptive threshold value is calculated; if the current pulse neuron output information is greater than or equal to the self-adaptive threshold value, the current pulse neuron output information is output, and the current self-adaptive threshold value variable is updated according to a first self-adaptive threshold value updating model; and otherwise the current pulse neuron output information is not output, and the current self-adaptive threshold value variable is updated according to a second self-adaptive threshold value updating model. According to the invention, the grant frequency of each neuron in the whole network can be effectively balanced, and the information processing capability of the pulse neural network is improved.

Description

technical field [0001] The invention relates to the technical field of artificial neural networks, in particular to an adaptive threshold neuron information processing method and system. Background technique [0002] Most of today's artificial neural network research is still implemented in von Neumann computer software and high-performance GPGPU (General Purpose Graphic Processing Units) platform. The hardware overhead, energy consumption and information of the whole process The processing speed is not optimistic. For this reason, the field of neuromorphic computing has developed rapidly in recent years, that is, using hardware circuits to directly construct neural networks to simulate the functions of the brain, trying to achieve a computing platform that is massively parallel, low-energy, and capable of supporting complex pattern learning. [0003] However, in traditional neuromorphic systems, how to effectively balance the firing frequency of each spiking neuron in the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/06
Inventor 裴京邓磊施路平吴臻志李国齐
Owner TSINGHUA UNIV
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