Utilization of SAM-based network for developing function approximation

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.