Title :
Adaptive Restructuring of Radial Basis Functions Using Integrate-and-Fire Neurons
Author :
Marvel, Jeremy A.
Author_Institution :
Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
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.
Keywords :
neural net architecture; radial basis function networks; stochastic processes; RBFNN model; adaptive restructuring; dynamic restructuring; hidden radial basis function re-centers; homeostasis; integrate-and-fire models; integrate-and-fire neurons; network structural parameters; neurobiology-based extension; radial basis function neural networks; stochastic parameter-space decision planes; synapse modulation; Adaptation models; Biological neural networks; Biological system modeling; Neurons; Training; Vectors; feed-forward networks; machine learning; neural networks; radial basis functions;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2014 13th International Conference on
Conference_Location :
Detroit, MI
DOI :
10.1109/ICMLA.2014.35
