title: Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System.
abstract: Optimal use of sensory information requires that the brain estimates the reliability of sensory cues, but the neural correlate of cue reliability relevant for behavior is not well defined. Here, we addressed this issue by examining how the reliability of spatial cue influences neuronal responses and behavior in the owl's auditory system. We show that the firing rate and spatial selectivity changed with cue reliability due to the mechanisms generating the tuning to the sound localization cue. We found that the correlated variability among neurons strongly depended on the shape of the tuning curves. Finally, we demonstrated that the change in the neurons' selectivity was necessary and sufficient for a network of stochastic neurons to predict behavior when sensory cues were corrupted with noise. This study demonstrates that the shape of tuning curves can stand alone as a coding dimension of environmental statistics.
  
  
  CONCLUSIONS
  In natural environments, sensory cues are often corrupted by noise and are therefore unreliable. To make the best decisions, the brain must estimate the degree to which a cue can be trusted. The behaviorally relevant neural correlates of cue reliability are debated. In this study, we used the barn owl's sound localization system to address this question. We demonstrated that the mechanisms that account for spatial selectivity also explained how neural responses changed with degraded signals. This allowed for the neurons' selectivity to capture cue reliability, influencing the population readout commanding the owl's sound-orienting behavior.
authors.affiliation: Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461, and fanny.cazettes@phd.einstein.yu.edu.
authors.forename: Fanny
authors.initials: F
authors.lastname: Cazettes
authors.affiliation: Department of Mathematics, Seattle University, Seattle, Washington 98122.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Fischer
authors.affiliation: Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461, and.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Peña
mesh_terms: D000161:Acoustic Stimulation; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001522:Behavior, Animal / Q000502:physiology; D003463:Cues; D005260:Female; D007245:Inferior Colliculi / Q000502:physiology; D009474:Neurons / Q000502:physiology; D013017:Sound Localization / Q000502:physiology*; D013269:Stochastic Processes; D020305:Strigiformes / Q000502:physiology*
keywords: Bayesian; barn owl; neural coding; population vector; reliability; sound localization