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