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Stimulus-dependent representational drift in primary visual cortex

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  • Tyler D. Marks

    (University of California)

  • Michael J. Goard

    (University of California
    University of California
    University of California)

Abstract

To produce consistent sensory perception, neurons must maintain stable representations of sensory input. However, neurons in many regions exhibit progressive drift across days. Longitudinal studies have found stable responses to artificial stimuli across sessions in visual areas, but it is unclear whether this stability extends to naturalistic stimuli. We performed chronic 2-photon imaging of mouse V1 populations to directly compare the representational stability of artificial versus naturalistic visual stimuli over weeks. Responses to gratings were highly stable across sessions. However, neural responses to naturalistic movies exhibited progressive representational drift across sessions. Differential drift was present across cortical layers, in inhibitory interneurons, and could not be explained by differential response strength or higher order stimulus statistics. However, representational drift was accompanied by similar differential changes in local population correlation structure. These results suggest representational stability in V1 is stimulus-dependent and may relate to differences in preexisting circuit architecture of co-tuned neurons.

Suggested Citation

  • Tyler D. Marks & Michael J. Goard, 2021. "Stimulus-dependent representational drift in primary visual cortex," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25436-3
    DOI: 10.1038/s41467-021-25436-3
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    Cited by:

    1. Hannah Muysers & Hung-Ling Chen & Johannes Hahn & Shani Folschweiller & Torfi Sigurdsson & Jonas-Frederic Sauer & Marlene Bartos, 2024. "A persistent prefrontal reference frame across time and task rules," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Zvi N. Roth & Elisha P. Merriam, 2023. "Representations in human primary visual cortex drift over time," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Han Chin Wang & Amy M. LeMessurier & Daniel E. Feldman, 2022. "Tuning instability of non-columnar neurons in the salt-and-pepper whisker map in somatosensory cortex," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Ravi Pancholi & Lauren Ryan & Simon Peron, 2023. "Learning in a sensory cortical microstimulation task is associated with elevated representational stability," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Christoph Stöckl & Yukun Yang & Wolfgang Maass, 2024. "Local prediction-learning in high-dimensional spaces enables neural networks to plan," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. Percy K. Mistry & Anthony Strock & Ruizhe Liu & Griffin Young & Vinod Menon, 2023. "Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network," Nature Communications, Nature, vol. 14(1), pages 1-21, December.

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