A neural data structure for novelty detection

Dasgupta, S., Sheehan, T. C., Stevens, C. F., Navlakha, S. (December 2018) A neural data structure for novelty detection. Proc Natl Acad Sci U S A, 115 (51). pp. 13093-13098. ISSN 0027-8424 (Public Dataset)

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URL: https://www.ncbi.nlm.nih.gov/pubmed/30509984
DOI: 10.1073/pnas.1814448115


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.

Item Type: Paper
Subjects: bioinformatics
organism description > animal > insect > Drosophila
bioinformatics > computational biology > algorithms
organism description > animal
organism description > animal behavior
bioinformatics > computational biology
organism description > animal > insect
bioinformatics > computational biology > algorithms > machine learning
organism description > animal behavior > odor recognition
organism description > animal behavior > olfactory
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 18 December 2018
Date Deposited: 06 Nov 2019 16:49
Last Modified: 20 Feb 2024 18:28
PMCID: PMC6304992
Related URLs:
Dataset ID:
  • Supplement https://www.pnas.org/content/suppl/2018/11/26/1814448115.DCSupplemental
URI: https://repository.cshl.edu/id/eprint/38636

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