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Optimal auditory cortex (A1) performance during sensory processing relies on intermediate arousal levels. This study reveals network mechanisms and phase transitions in neural activity that explain this inverted-U relationship.

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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 sensory processing.
  • To identify neural correlates of arousal-dependent coding in the auditory cortex.

Main Methods:

  • Recorded neural activity from the auditory cortex (A1) of behaving mice.
  • Tracked arousal levels using pupillometry during passive tone presentation.
  • Utilized a spiking network model with a clustered architecture to explain observed data.

Main Results:

  • Tone discriminability in A1 ensembles was optimal at intermediate arousal levels.
  • Arousal-dependent coding was explained by a model near a phase transition between multi-attractor and single-attractor network states.
  • Observed arousal-induced reductions in neural variability and stimulus-induced variability quenching.

Conclusions:

  • Elucidated computational principles of arousal, sensory processing, and neural variability interactions.
  • Suggested a role for network phase transitions in nonlinear cortical computations.