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Neuromorphic architectures are ideally suited for the implementation of smartsensors able to react, learn, and respond to a changing environment. Our workuses the insect brain as a model to understand how heterogeneous architectures,incorporating different types of neurons and encodings, can be leveraged tocreate systems integrating input processing, evaluation, and response. Here weshow how the combination of time and rate encodings can lead to fast sensorsthat are able to generate a hypothesis on the input in only a few cycles andthen use that hypothesis as secondary input for more detailed analysis.

Fast, Smart Neuromorphic Sensors Based on Heterogeneous Networks and Mixed Encodings