September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior
Author Affiliations & Notes
  • Kohitij Kar
    McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 01239, USA
    Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, 01239, USA
  • Martin Schrimpf
    McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 01239, USA
  • Kailyn Schmidt
    McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 01239, USA
  • James J DiCarlo
    McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 01239, USA
    Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, 01239, USA
  • Footnotes
    Acknowledgements  This research was supported by the Office of Naval Research MURI-114407 (J.J.D), and by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.
Journal of Vision September 2021, Vol.21, 2489. doi:https://doi.org/10.1167/jov.21.9.2489
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Kohitij Kar, Martin Schrimpf, Kailyn Schmidt, James J DiCarlo; Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision 2021;21(9):2489. doi: https://doi.org/10.1167/jov.21.9.2489.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Distributed activity patterns across multiple brain areas (e.g., V4, IT) enable primates to accurately identify visual objects. To strengthen our inferences about the causal role of underlying brain circuits, it is necessary to develop targeted neural perturbation strategies that enable discrimination amongst competing models. To probe the role of area V4 in core object recognition, we expressed inhibitory DREADDs in neurons within a 5x5 mm subregion of V4 cortex via multiple viral injections (AAV8-hSyn-hM4Di-mCherry; two macaques). To assay for successful neural suppression, we recorded from a multi-electrode array implanted over the transfected V4. We also recorded from multi-electrode arrays in the IT cortex (the primary feedforward target of V4), while simultaneously measuring the monkeys’ behavior during object discrimination tasks. We found that systemic (intramuscular) injection of the DREADDs activator (CNO) produced reversible reductions (~20%) in image-evoked V4 responses compared to the control condition (saline injections). Monkeys showed significant behavioral performance deficits upon CNO injections (compared to saline), which were larger when the object position overlapped with the RF estimates of the transfected V4 neurons. This is consistent with the hypothesis that the suppressed V4 neurons are critical to this behavior. Furthermore, we observed commensurate deficits in the linearly-decoded estimates of object identity from the IT population activity (post-CNO). To model the perturbed brain circuitry, we used a primate brain-mapped artificial neural network (ANN) model (CORnet-S) that supports object recognition. We “lesioned” the model’s corresponding V4 subregion by modifying its weights such that the responses matched a subset of our experimental V4 measurements (post-CNO). Indeed, the lesioned model better predicted the measured (held-out) V4 and IT responses (post-CNO), compared to the model's non-lesioned version, validating our approach. In the future, our approach allows us to discriminate amongst competing mechanistic brain models, while the data provides constraints to guide more accurate alternatives.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×