DocumentCode :
2851674
Title :
The Properties of Spike-Rate Perceptron with Super-Poisson Input
Author :
Wang, Yonglin ; Xiang, Xuyan ; Deng, Yingchun
Author_Institution :
Coll. of Comput. Sci., Hunan Univ. of Arts & Sci., China
fYear :
2010
fDate :
13-15 Aug. 2010
Firstpage :
1
Lastpage :
5
Abstract :
We present the non-linear properties of Spike-Rate Perceptron with super-Poisson inputs, which employs both first and second statistical representation, i.e. the means, variances and correlations of the synaptic input. It shows that such perceptron, even a single neuron, is able to perform various complex non-linear tasks like the XOR problem. Here such perceptron offers a significant advantage over classical models, in that they include both the mean and the variance of the input signal.
Keywords :
perceptrons; statistical analysis; stochastic processes; XOR problem; single neuron; spike rate perceptron; statistical representation; super Poisson input; Approximation methods; Biological system modeling; Computational modeling; Correlation; Equations; Mathematical model; Neurons; Integrate-and-fire model; non-linear properties; second order statistics; super-Poisson input;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7575-9
Type :
conf
DOI :
10.1109/BIFE.2010.11
Filename :
5621716
Link To Document :
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