A sequential learning algorithm for a Minimal Spiking Neural Network (MSNN) classifier

Shirin Dora, S. Suresh, N. Sundararajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this paper, we develop a new sequential learning algorithm for a spiking neural network classifier. The algorithm handles the input features that are not in the form of a spike train but in a real-valued (analog) form. The sequential learning algorithm evolves the number of spiking neuron automatically based on the information present in the current sample and results in a compact architecture. Hence, it is referred to as a Minimal Spiking Neural Network (MSNN). The learning algorithm can either add a new neuron to the network or update the parameters of the existing neurons based on the information contained in the arriving samples. The update rule uses excitatory/inhibitatory rule to capture the knowledge contained in the current sample. Performance evaluation of the proposed MSNN is presented using two benchmark problems from the UCI machine learning repository, namely, the Iris flower classification and Wisconsin breast cancer problem and the results are compared with other existing spiking neural algorithms like SpikeProp, MuSpiNN and Multi-spike learning algorithms. The results clearly indicate the better performance of MSNN with a compact architecture.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2415-2421
Number of pages7
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

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  • Cite this

    Dora, S., Suresh, S., & Sundararajan, N. (2014). A sequential learning algorithm for a Minimal Spiking Neural Network (MSNN) classifier. In Proceedings of the International Joint Conference on Neural Networks (pp. 2415-2421). [6889775] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889775