Reduction of Processing Time for Wireless Spiking Neural Network Using Wireless Communication Devices for IoT <Abstract> We proposed the concept of implementing Spiking Neural Network (SNN) dynamics in information networks, such as Internet of Things (IoT) networks, by incorporating information processing by neurons in IoT devices. In this manuscript, we investigate the relationship between the TDMA (Time Division Multiple Access) subslot width and the learning ability of a wireless SNN (WSNN) in the WSNN system with TDMA and IEEE802.15.4e IoT communication. we investigate the relationship between the TDMA subslot width and the learning ability of a WSNN, which combines TDMA (Time Division Multiple Access) with IEEE802.15.4e-compliant wireless communications for the IoT. It becomes clear that the lower bound of the subslot width is closely related to the number of network firings. Besides, it is shown that there is a threshold that separates successful learning from unsuccessful learning. |