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A neural theory for counting memories

Author

Listed:
  • Sanjoy Dasgupta

    (University of California San Diego)

  • Daisuke Hattori

    (UT Southwestern Medical Center)

  • Saket Navlakha

    (Cold Spring Harbor Laboratory)

Abstract

Keeping track of the number of times different stimuli have been experienced is a critical computation for behavior. Here, we propose a theoretical two-layer neural circuit that stores counts of stimulus occurrence frequencies. This circuit implements a data structure, called a count sketch, that is commonly used in computer science to maintain item frequencies in streaming data. Our first model implements a count sketch using Hebbian synapses and outputs stimulus-specific frequencies. Our second model uses anti-Hebbian plasticity and only tracks frequencies within four count categories (“1-2-3-many”), which trades-off the number of categories that need to be distinguished with the potential ethological value of those categories. We show how both models can robustly track stimulus occurrence frequencies, thus expanding the traditional novelty-familiarity memory axis from binary to discrete with more than two possible values. Finally, we show that an implementation of the “1-2-3-many” count sketch exists in the insect mushroom body.

Suggested Citation

  • Sanjoy Dasgupta & Daisuke Hattori & Saket Navlakha, 2022. "A neural theory for counting memories," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33577-2
    DOI: 10.1038/s41467-022-33577-2
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    References listed on IDEAS

    as
    1. Sophie J. C. Caron & Vanessa Ruta & L. F. Abbott & Richard Axel, 2013. "Random convergence of olfactory inputs in the Drosophila mushroom body," Nature, Nature, vol. 497(7447), pages 113-117, May.
    2. Stijn Cassenaer & Gilles Laurent, 2007. "Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts," Nature, Nature, vol. 448(7154), pages 709-713, August.
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