@article{3619,
  author = {Minoru Motoki, Hirohito Shintani, Kazunori Matsuo, Thomas Martin McGinnity},
  title = {Utilization of SAM-based Network for Developing Function Approximation},
  journal = {Journal of Digital Information Management},
  year = {2022},
  volume = {20},
  number = {4},
  doi = {https://doi.org/10.6025/jdim/2022/20/4/148-155},
  url = {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 “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.},
}