A novel single neuron perceptron with universal approximation and XOR computation properties.

Abstract:

:We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

journal_name

Comput Intell Neurosci

authors

Lotfi E,Akbarzadeh-T MR

doi

10.1155/2014/746376

subject

Has Abstract

pub_date

2014-01-01 00:00:00

pages

746376

eissn

1687-5265

issn

1687-5273

journal_volume

2014

pub_type

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