01320nas a2200169   4500008004100000245007500041210006900116260001200185300001200197490000700209520079400216100001901010700002301029700002101052700003001073856004701103       2022                        eng d00aUtilization of SAM-based network for developing function approximation0 aUtilization of SAMbased network for developing function approxim  c12/2022  a148-1550 v203 a<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 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.</span></p>
1 aMotoki, Minoru1 aShintani, Hirohito1 aMatsuo, Kazunori1 aMcGinnity, Thomas, Martin  uhttp://www.dline.info/download.php?sn=3619