Habituation as a Neural Algorithm for Online Odor Discrimination

Shen, Y., Dasgupta, S., Navlakha, S. (May 2020) Habituation as a Neural Algorithm for Online Odor Discrimination. Proceedings of the National Academy of Sciences of the United States of America, 117 (22). pp. 12402-12410. ISSN 0027-8424

URL: https://pubmed.ncbi.nlm.nih.gov/32430320/
DOI: 10.1073/pnas.1915252117


Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural 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 behavior > habituation
organism description > animal > insect
organism description > animal behavior > memory
organism description > animal behavior > olfactory
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Adrian Gomez
Date: 19 May 2020
Date Deposited: 21 May 2020 17:09
Last Modified: 01 Feb 2024 18:27
PMCID: PMC7275754
Related URLs:
URI: https://repository.cshl.edu/id/eprint/39472

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