title: 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.
authors.affiliation: Department of Computer Engineering, Torbat-e-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran.
authors.forename: Ehsan
authors.identifier: 0000-0001-6427-0825
authors.initials: E
authors.lastname: Lotfi
authors.affiliation: Electrical and Computer Engineering Departments, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Iran.
authors.forename: M-R
authors.initials: MR
authors.lastname: Akbarzadeh-T
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology*; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated