@article {756,
	title = {Utilization of SAM-based network for developing function approximation},
	journal = {Journal of Digital Information Management},
	volume = {20},
	year = {2022},
	month = {12/2022},
	pages = {148-155},
	type = {Research},
	chapter = {148},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">We have previously reported progress in developing a multilayer SAM spiking neural network and a training algorithm, suitable for implementation on an FPGA with {\^a}{\texteuro}{\oe}On- Chip Learning{\^a}{\texteuro}. 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 {\^a}{\texteuro}\~{}NFR-coding{\^a}{\texteuro}{\texttrademark}. We demonstrate {\^a}{\texteuro}{\oe}interpolated XOR{\^a}{\texteuro} 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.</span></p>
},
	doi = {https://doi.org/10.6025/jdim/2022/20/4/148-155},
	url = {http://www.dline.info/download.php?sn=3619},
	author = {Minoru Motoki and Hirohito Shintani and Kazunori Matsuo and Thomas Martin McGinnity}
}
