A neural data structure for novelty detection.

TitleA neural data structure for novelty detection.
Publication TypeJournal Article
Year of Publication2018
AuthorsDasgupta S, Sheehan TC, Stevens CF, Navlakha S
JournalProc Natl Acad Sci U S A
Date Published2018 12 18
KeywordsAlgorithms, Animals, Drosophila, Models, Biological, Nerve Net, Neural Networks (Computer), Odorants, Olfactory Pathways

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.

Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID30509984
PubMed Central IDPMC6304992
Grant ListR01 DC017695 / DC / NIDCD NIH HHS / United States
IRG Funded