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 Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Lotfi E,Akbarzadeh-T MRdoi
10.1155/2014/746376subject
Has Abstractpub_date
2014-01-01 00:00:00pages
746376eissn
1687-5265issn
1687-5273journal_volume
2014pub_type
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