Time-series prediction with single integrate-and-fire neuron

Authors:
A. Yadav;D. Mishra;R. N. Yadav;S. Ray;P. K. Kalra
Affiliations:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, India;Department of Electrical Engineering, Indian Institute of Technology Kanpur, India;Department of Electrical Engineering, Indian Institute of Technology Kanpur, India;Department of Electrical Engineering, Indian Institute of Technology Kanpur, India;Department of Electrical Engineering, Indian Institute of Technology Kanpur, India
Venue:
Applied Soft Computing
Year:
2007

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Abstract

In this paper, a learning algorithm for a single integrate-and-fire neuron (IFN) is proposed and tested for various applications in which a multilayer perceptron neural network is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and time-series prediction have been illustrated. It is observed that the inclusion of some more biological phenomenon in an artificial neural network can make it more powerful.