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Adaptive Restructuring of Radial Basis Functions Using Integrate-and-Fire Neurons

Published

Author(s)

Jeremy A. Marvel

Abstract

This paper proposes a neurobiology-based extension of integrate-and-fire models of Radial Basis Function Neural Networks (RBFNN) that adapts to novel stimuli by means of dynamic restructuring of the network's structural parameters. The new architecture automatically balances synapses modulation, re-centers hidden Radial Basis Functions (RBFs), and stochastically shifts parameter-space decision planes to maintain homeostasis. Example results are provided throughout the paper to illustrate the effects of changes to the RBFNN model.
Proceedings Title
13th International Conference on Machine Learning and Applications (ICMLA'14)
Conference Dates
December 3-6, 2014
Conference Location
Detroit, MI

Keywords

Machine learning, Radial basis functions, neural networks

Citation

Marvel, J. (2014), Adaptive Restructuring of Radial Basis Functions Using Integrate-and-Fire Neurons, 13th International Conference on Machine Learning and Applications (ICMLA'14), Detroit, MI, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=917224 (Accessed March 2, 2024)
Created December 6, 2014, Updated February 19, 2017