Utilization of SAM-based network for developing function approximation
Minoru Motoki, Hirohito Shintani, Kazunori Matsuo, Thomas Martin McGinnity · Journal of Digital Information Management · 2022
We have previously reported progress in developing a multilayer SAM spiking neural network and a training algorithm, suitable for implementation on an FPGA with "On-Chip Learning".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 'NFR-coding'.We demonstrate "interpolated XOR" 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.