Modeling and Training Radial Basis Functions with Integrate-and-Fire Neurons

Various “biologically-inspired” models of computation have been developed over the years. Though their inception may have been inspired by biology, most are not biologically plausible. The concepts of training neural networks by back propagation and global observers occurs nowhere in nature. In this paper, a novel variation on a Radial-Basis Function (RBF) network is proposed that is biologically plausible as supported by the literature. A case study is presented that demonstrate the efficacy of this method in producing functional approximations of difficult problems.