|Title||A neural data structure for novelty detection.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Dasgupta S, Sheehan TC, Stevens CF, Navlakha S|
|Journal||Proc Natl Acad Sci U S A|
|Date Published||2018 12 18|
|Keywords||Algorithms, 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 Journal||Proc. Natl. Acad. Sci. U.S.A.|
|PubMed Central ID||PMC6304992|
|Grant List||R01 DC017695 / DC / NIDCD NIH HHS / United States|
A neural data structure for novelty detection.