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Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex

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  • Diego Mendoza-Halliday

    (McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT)
    McGill University)

  • Julio C. Martinez-Trujillo

    (Pharmacology and Psychiatry, Robarts Research Institute, Brain and Mind Institute, Schulich School of Medicine and Dentistry, Western University)

Abstract

The primate lateral prefrontal cortex (LPFC) encodes visual stimulus features while they are perceived and while they are maintained in working memory. However, it remains unclear whether perceived and memorized features are encoded by the same or different neurons and population activity patterns. Here we record LPFC neuronal activity while monkeys perceive the motion direction of a stimulus that remains visually available, or memorize the direction if the stimulus disappears. We find neurons with a wide variety of combinations of coding strength for perceived and memorized directions: some neurons encode both to similar degrees while others preferentially or exclusively encode either one. Reading out the combined activity of all neurons, a machine-learning algorithm reliably decode the motion direction and determine whether it is perceived or memorized. Our results indicate that a functionally diverse population of LPFC neurons provides a substrate for discriminating between perceptual and mnemonic representations of visual features.

Suggested Citation

  • Diego Mendoza-Halliday & Julio C. Martinez-Trujillo, 2017. "Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex," Nature Communications, Nature, vol. 8(1), pages 1-13, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15471
    DOI: 10.1038/ncomms15471
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    Cited by:

    1. J. L. Amengual & F. Di Bello & S. Ben Hadj Hassen & Suliann Ben Hamed, 2022. "Distractibility and impulsivity neural states are distinct from selective attention and modulate the implementation of spatial attention," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    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.

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