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.

Optimal nonlinear cue integration for sound localization

“…For cue reliability to influence behavior, a decrease in reliability must cause an increase in tuning widths for that cue. It has been shown that changing the reliability of IPD causes IPD tuning curves to widen, consistent with the widening of the likelihood function as reliability changes (Cazettes et al, 2016). This is distinct from a reweighting of inputs in linear neural responses that is predicted by the PPC model (Fetsch et al, 2011).…”

“…While this is suboptimal, it simplifies the probability distribution that the brain must learn while representing the statistical relationship between the environment and the cues, which is the central component of the perceptual inference problem. Furthermore, for large populations, inferences made using the low-dimensional distribution ( p X|C 1 , C 2 ( X |C 1 , C 2 ) may closely approximate inferences made using the high-dimensional distribution ( p X| r 1 , r 2 ( X | r 1 , r 2 ) (Cazettes et al, 2016). …”

“…We have also found experimental support for the likelihood being represented in the pattern of activity across the population. In particular, the selectivity of the neurons decreases when sensory noise increases, so that activity is spread over more of the map (Cazettes et al, 2016). Moreover, the selectivity of neurons is lower in the periphery of the space map, where IPD is a less reliable cue.…”

Natural ITD statistics predict human auditory spatial perception

“…Previous reports have investigated the selectivity of midbrain neurons to the variability of spatial cues in the owl's auditory system (Cazettes et al 2014;Fischer and Peña 2017). These studies provided evidence of how sensory reliability could be represented (Fischer and Peña 2011;Rich et al 2015;Cazettes et al 2016) and integrated into an adaptive behavioral command (Cazettes et al 2018). Although the ITD statistics relevant to owls and humans differ based on the frequency range over which ITD is detected, and the coding of ITD cannot be assumed identical across species (Schnupp and Carr 2009), results of the present study support the hypothesis that specific ITD statistics, ITDrc and ITDv, determine human sound localization discriminability and novelty detection of acoustic spatial deviants.…”

Cracking an improbable sensory map

“…Observations of the owl's brain and behavior suggest that the answer is 'very much'. For example, the sound localization system of the barn owl implements various mathematical operations, such as multiplication (Pena and Konishi, 2001), averaging (Christianson and Peña, 2006), crosscorrelation (Saberi et al, 1998;Fischer et al, 2008) and Bayesian inference (Cazettes et al, 2016;Fischer and Peña, 2011). These operations had been predicted by models of human psychophysics (Jeffress et al, 1962;Licklider, 1959;Sayers and Cherry, 1957;Stern and Steven Colburn, 1978;Stern et al, 1988), but never demonstrated in neural responses.…”