Cutting-edge autonomous systems demonstrate outstanding results in many important tasks requiring intelligent data processing under well-known conditions. However, the performance of these systems may drastically deteriorate when the data are perturbed, or the environment dynamically changes, either due to natural or man-made disturbances. The challenges are especially daunting in edge computing scenarios and on-board applications with limited resources, i.e., data/ energy/ computational power constraints, when decisions must be made rapidly and in a robust way. A neuromorphic perspective provides useful insights under such conditions. Human brains are efficient devices using 20W power (like a light bulb), which is many orders of magnitudes less than the power consumption of today’s supercomputers requiring MWs to innovatively solve a specific Deep AI learning task. Brains use spatio-temporal oscillations to implement pattern-based computing, going beyond the sequential symbol manipulation paradigm of traditional Turing machines. Neuromorphic spiking chips gain popularity in the field. Application examples include autonomous on-board signal processing and control, distributed sensor systems, autonomous robot navigation and control, and rapid response to emergencies.