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

Nat Commun. 2015 Feb 4:6:6177. doi: 10.1038/ncomms7177.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Choice Behavior / physiology*
  • Decision Making / physiology
  • Macaca mulatta
  • Male
  • Models, Psychological
  • Motion Perception / physiology
  • Nerve Net / anatomy & histology
  • Nerve Net / physiology*
  • Neurons / physiology
  • Parietal Lobe / anatomy & histology
  • Parietal Lobe / physiology*
  • Photic Stimulation
  • Probability
  • Temporal Lobe / anatomy & histology
  • Temporal Lobe / physiology*
  • Visual Cortex / anatomy & histology
  • Visual Cortex / physiology*
  • Visual Perception / physiology