Introduction -- Bridging the accuracy gap between SNNs and DNNs via the use of pre-processing for radar applications -- Design of a drone platform for sensor fusion data acquisition -- A top-down approach to SNN-STDP networks -- Sensor-fusion SLAM with continual STDP learning -- Continually learning people detection from DVS data -- Active inference in Hebbian learning networks -- Conclusions and future work -- Appendix.

This book provides novel theoretical foundations and experimental demonstrations of Spiking Neural Networks (SNNs) in tasks such as radar gesture recognition for IoT devices and autonomous drone navigation using a fusion of retina-inspired event-based camera and radar sensing. The authors describe important new findings about the Spike-Timing-Dependent Plasticity (STDP) learning rule, which is widely believed to be one of the key learning mechanisms taking place in the brain. Readers will be enabled to create novel classes of edge AI and robotics applications, using highly energy- and area-efficient SNNs. Describes systematic design of SNN-STDP systems that significantly outperform prior works in terms of system accuracy; Presents the design of a first-of-its-kind SNN-STDP-based Simultaneous Localization and Mapping system for drones; Includes real examples of tasks such as radar-based gesture recognition and drone navigation.