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Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

Author

Listed:
  • Klaus Wimmer

    (Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS))

  • Albert Compte

    (Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS))

  • Alex Roxin

    (Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
    Centre de Recerca Matemàtica (CRM), Campus de Bellaterra)

  • Diogo Peixoto

    (Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown
    Stanford University)

  • Alfonso Renart

    (Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown)

  • Jaime de la Rocha

    (Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS))

Abstract

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

Suggested Citation

  • Klaus Wimmer & Albert Compte & Alex Roxin & Diogo Peixoto & Alfonso Renart & Jaime de la Rocha, 2015. "Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT," Nature Communications, Nature, vol. 6(1), pages 1-13, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7177
    DOI: 10.1038/ncomms7177
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    Cited by:

    1. Audrey J Sederberg & Aurélie Pala & He J V Zheng & Biyu J He & Garrett B Stanley, 2019. "State-aware detection of sensory stimuli in the cortex of the awake mouse," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-24, May.
    2. Kaushik J Lakshminarasimhan & Alexandre Pouget & Gregory C DeAngelis & Dora E Angelaki & Xaq Pitkow, 2018. "Inferring decoding strategies for multiple correlated neural populations," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-40, September.
    3. Richard D Lange & Ankani Chattoraj & Jeffrey M Beck & Jacob L Yates & Ralf M Haefner, 2021. "A confirmation bias in perceptual decision-making due to hierarchical approximate inference," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-30, November.
    4. Andrew M. Clark & David C. Bradley, 2022. "A neural correlate of perceptual segmentation in macaque middle temporal cortical area," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    5. Christopher Ebsch & Robert Rosenbaum, 2018. "Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-28, March.
    6. Lluís Hernández-Navarro & Ainhoa Hermoso-Mendizabal & Daniel Duque & Jaime de la Rocha & Alexandre Hyafil, 2021. "Proactive and reactive accumulation-to-bound processes compete during perceptual decisions," Nature Communications, Nature, vol. 12(1), pages 1-15, December.

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