Utilization of SAM-based Network for Developing Function Approximation
Minoru Motoki, Hirohito Shintani, Kazunori Matsuo, Thomas Martin McGinnity
Journal of Digital Information Management, Vol. 20, No. 4, 2022.
DOI: https://doi.org/10.6025/jdim/2022/20/4/148-155
Link: https://www.dline.info/fpaper/jdim/v20i4/jdimv20i4_3.pdf
Abstract:
We have previously reported progress in developing a multilayer SAM spiking neural network and a training algorithm, suitable for implementation on an FPGA with â€œOn- Chip Learningâ€. Here we report on utilization of a SAM -based network for continuous function approximation, which to date has proved difficult to achieve on a LIF type spiking neural network, by using a spike coding approach called â€˜NFR-codingâ€™. We demonstrate â€œinterpolated XORâ€ and 3-polynominal function approximation of this SAM network in computational experiments. It is demonstrated that the SAM network has the capability to perform these function approximations to high accuracy.