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

arXiv: https://arxiv.org/abs/2104.04121 · HTML: https://mlsanity.com/paper/2104.04121/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Abstract (arXiv)

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.