DocumentCode :
2152133
Title :
Remembering Key Features of Visual Images Based on Spike Timing Dependent Plasticity of Spiking Neurons
Author :
Wu, QingXiang ; Cai, Rongtai ; McGinnity, T.M. ; Maguire, Liam ; Harkin, Jim
Author_Institution :
Sch. of Phys. & Optoelectron. Technol., Fujian Normal Univ., Fuzhou, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
5
Abstract :
The brain has the powerful capability of remembering key features of images. Based on the principle of spike timing dependent plasticity of spiking neurons and the ON/OFF pathways in the visual system, a spiking neural network is proposed to remember key features of visual images. The simulation results show that the network is capable of remembering key features according to a learning rule based on spike timing dependent plasticity. The principle of the network can be used to explain how a spiking neuron-based system can store the key features of visual images. Furthermore, the network can be applied to spiking neuron based artificial intelligent systems to support the processing visual images.
Keywords :
artificial intelligence; brain; neural nets; neurophysiology; visual perception; artificial intelligent systems; learning rule; spike timing dependent plasticity; spiking neural network; spiking neurons; visual images; Artificial intelligence; Biological neural networks; Brain modeling; Equations; Humans; Intelligent systems; Neurons; Retina; Timing; Visual system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5303978
Filename :
5303978
Link To Document :
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