2018/12/03 by Sanjoy Dasgupta, Timothy C. Sheehan, Charles F. Stevens +1 · 57 citations
Computer Science · Biochemistry, Genetics and Molecular Biology · Psychology · #Anomaly Detection Techniques and Applications #Machine Learning in Bioinformatics #Network Security and Intrusion Detection #Novelty detection #Novelty #Computer science #Artificial intelligence #Artificial neural network #Pattern recognition (psychology) #Psychology
paper · pdf · doi:10.1073/pnas.1814448115
published in Proceedings of the National Academy of Sciences 115(51), 13093-13098 (National Academy of Sciences)
openalex publication_date 2018/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/08
Novelty detection is a fundamental biological problem that organisms must solve to determine whether a given stimulus departs from those previously experienced. In computer science, this problem is solved efficiently using a data structure called a Bloom filter. We found that the fruit fly olfactory circuit evolved a variant of a Bloom filter to assess the novelty of odors. Compared with a traditional Bloom filter, the fly adjusts novelty responses based on two additional features: the similarity of an odor to previously experienced odors and the time elapsed since the odor was last experienced. We elaborate and validate a framework to predict novelty responses of fruit flies to given pairs of odors. We also translate insights from the fly circuit to develop a class of distance- and time-sensitive Bloom filters that outperform prior filters when evaluated on several biological and computational datasets. Overall, our work illuminates the algorithmic basis of an important neurobiological problem and offers strategies for novelty detection in computational systems.