Reduction of processing time for wireless SNN 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. However, the relationship between communication characteristics and the learning ability of SNNs has not been investigated in detail yet. 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 ommunication. , we investigate the relationship between the TDMA subslot width and the learning ability of a wireless SNN (Wireless SNN :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. These characteristics are the essence of using CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) compliant communication devices. This suggests the importance of evaluating WSNNs using communication devices that can communicate simultaneously. |