Event-Driven Continuous STDP Learning With Deep Structure for Visual Pattern Recognition

Daqi Liu

doi:10.1109/TCYB.2018.2801476

2019-04-01

Daqi Liu

Shigang Yue *

DOI：10.1109/TCYB.2018.2801476

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摘要

En 中文

Human beings can achieve reliable and fast visual pattern recognition with limited time and learning samples. Underlying this capability, ventral stream plays an important role in object representation and form recognition. Modeling the ventral steam may shed light on further understanding the visual brain in humans and building artificial vision systems for pattern recognition. The current methods to model the mechanism of ventral stream are far from exhibiting fast, continuous, and event-driven learning like the human brain. To create a visual system similar to ventral stream in human with fast learning capability, in this paper, we propose a new spiking neural system with an event-driven continuous spike timing dependent plasticity (STDP) learning method using specific spiking timing sequences. Two novel continuous input mechanisms have been used to obtain the continuous input spiking pattern sequence. With the event-driven STDP learning rule, the proposed learning procedure will be activated if the neuron receive one pre- or post-synaptic spike event. The experimental results on MNIST database show that the proposed method outperforms all other methods in fast learning scenarios and most of the current models in exhaustive learning experiments.

Keyword:

Continuous learning

deep learning

event-driven spike timing dependent plasticity (STDP)

spiking neural network (SNN)

visual pattern recognition

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期刊

IEEE Transactions on Cybernetics

IF:

10.5

论文数:

1.1W

被引数:

5.0W

机构

University of Lincoln

学者数:

2.6K

论文数: 2.6K

被引数: 3.9K

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