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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Perceptual decision-making performance shows an inverted-U relationship with arousal.
  • The neural network mechanisms driving this phenomenon are not well understood.

Purpose of the Study:

  • To investigate the network mechanisms underlying the inverted-U relationship between arousal and performance.
  • To identify neural correlates of arousal-dependent coding in the auditory cortex.

Main Methods:

  • Recorded neural activity from the auditory cortex (A1) in behaving mice during auditory tone presentation.
  • Tracked arousal levels using pupillometry.
  • Developed and analyzed a spiking network model with a clustered architecture.

Main Results:

  • Tone discriminability in A1 ensembles was optimal at intermediate arousal levels.
  • A spiking network model demonstrated that optimal discriminability occurs near a phase transition.
  • Observed arousal-induced reductions in neural variability and stimulus-induced variability quenching in empirical data.

Conclusions:

  • Arousal-dependent coding in the auditory cortex is explained by network dynamics near a phase transition.
  • This transition shifts between metastable cluster dynamics (low arousal) and a single-attractor state (high arousal).
  • Findings elucidate computational principles linking arousal, sensory processing, and neural variability, suggesting a role for phase transitions in cortical computations.