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Neuronal correlates of parametric working memory in the prefrontal cortex

Author

Listed:
  • Ranulfo Romo

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

  • Carlos D. Brody

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

  • Adrián Hernández

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

  • Luis Lemus

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

Abstract

Humans and monkeys have similar abilities to discriminate the difference in frequency between two mechanical vibrations applied sequentially to the fingertips1,2,3. A key component of this sensory task is that the second stimulus is compared with the trace left by the first (base) stimulus, which must involve working memory. Where and how is this trace held in the brain? This question was investigated by recording from single neurons in the prefrontal cortex of monkeys while they performed the somatosensory discrimination task. Here we describe neurons in the inferior convexity of the prefrontal cortex whose discharge rates varied, during the delay period between the two stimuli, as a monotonic function of the base stimulus frequency. We describe this as ‘monotonic stimulus encoding’, and we suggest that the result may generalize: monotonic stimulus encoding may be the basic representation of one-dimensional sensory stimulus quantities in working memory. Thus we predict that other behavioural tasks that require ordinal comparisons between scalar analogue stimuli would give rise to monotonic responses similar to those reported here.

Suggested Citation

  • Ranulfo Romo & Carlos D. Brody & Adrián Hernández & Luis Lemus, 1999. "Neuronal correlates of parametric working memory in the prefrontal cortex," Nature, Nature, vol. 399(6735), pages 470-473, June.
    • Handle: RePEc:nat:nature:v:399:y:1999:i:6735:d:10.1038_20939
    DOI: 10.1038/20939
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    Cited by:

    1. Juan Linde-Domingo & Bernhard Spitzer, 2024. "Geometry of visuospatial working memory information in miniature gaze patterns," Nature Human Behaviour, Nature, vol. 8(2), pages 336-348, February.
    2. Francesco Ceccarelli & Lorenzo Ferrucci & Fabrizio Londei & Surabhi Ramawat & Emiliano Brunamonti & Aldo Genovesio, 2023. "Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Gabriel D Puccini & Maria V Sanchez-Vives & Albert Compte, 2007. "Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits," PLOS Computational Biology, Public Library of Science, vol. 3(5), pages 1-13, May.
    4. Yue Liu & Xiao-Jing Wang, 2024. "Flexible gating between subspaces in a neural network model of internally guided task switching," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    5. Kaushik J. Lakshminarasimhan & Eric Avila & Xaq Pitkow & Dora E. Angelaki, 2023. "Dynamical latent state computation in the male macaque posterior parietal cortex," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    6. Sacha Jennifer van Albada & Moritz Helias & Markus Diesmann, 2015. "Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-37, September.
    7. Brian DePasquale & Christopher J Cueva & Kanaka Rajan & G Sean Escola & L F Abbott, 2018. "full-FORCE: A target-based method for training recurrent networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
    8. Xin Wei Chia & Jian Kwang Tan & Lee Fang Ang & Tsukasa Kamigaki & Hiroshi Makino, 2023. "Emergence of cortical network motifs for short-term memory during learning," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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