2021/04/08 by Ángel Yanguas-Gil, Angel Yanguas-Gil, Yanguas-Gil, Angel
Computer Science · Engineering · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Neural Networks and Applications #Neural Networks and Reservoir Computing #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.NE
paper · pdf · doi:10.48550/arxiv.2104.04121
Paper accepted in GOMACTech 2018
openalex publication_date 2021/04/08 · arxiv created 2021/04/09 · arxiv updated 2021/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.