Sensory systems preferentially strengthen responses to stimuli based on their reliability at conveying accurate information. While previous reports demonstrate that the brain reweighs cues based on ...dynamic changes in reliability, how the brain may learn and maintain neural responses to sensory statistics expected to be stable over time is unknown. The barn owl's midbrain features a map of auditory space where neurons compute horizontal sound location from the interaural time difference (ITD). Frequency tuning of midbrain map neurons correlates with the most reliable frequencies for the neurons' preferred ITD (Cazettes et al., 2014). Removal of the facial ruff led to a specific decrease in the reliability of high frequencies from frontal space. To directly test whether permanent changes in ITD reliability drive frequency tuning, midbrain map neurons were recorded from adult owls, with the facial ruff removed during development, and juvenile owls, before facial ruff development. In both groups, frontally tuned neurons were tuned to frequencies lower than in normal adult owls, consistent with the change in ITD reliability. In addition, juvenile owls exhibited more heterogeneous frequency tuning, suggesting normal developmental processes refine tuning to match ITD reliability. These results indicate causality of long-term statistics of spatial cues in the development of midbrain frequency tuning properties, implementing probabilistic coding for sound localization.
Bayesian models have proven effective in characterizing perception, behavior, and neural encoding across diverse species and systems. The neural implementation of Bayesian inference in the barn owl's ...sound localization system and behavior has been previously explained by a non-uniform population code model. This model specifies the neural population activity pattern required for a population vector readout to match the optimal Bayesian estimate. While prior analyses focused on trial-averaged comparisons of model predictions with behavior and single-neuron responses, it remains unknown whether this model can accurately approximate Bayesian inference on single trials under varying sensory reliability, a fundamental condition for natural perception and behavior. In this study, we utilized mathematical analysis and simulations to demonstrate that decoding a non-uniform population code via a population vector readout approximates the Bayesian estimate on single trials for varying sensory reliabilities. Our findings provide additional support for the non-uniform population code model as a viable explanation for the barn owl's sound localization pathway and behavior.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Natural environments are filled with an abundance of sensory stimuli the brain can use to perceive and create an internal representation of the world. However, sensory information may not be ...particularly useful and can lead to incorrect or inaccurate percepts. A common strategy to deal with this potential problem is to preferentially use stimulus cues that provide accurate information, while ignoring the less informative cues. Previous psychophysical and neurophysiological work have shown that a wide range of species are capable of learning the relative reliabilities of cues and using the most reliable to guide behavior. However, many gaps in the understanding of the brain mechanisms underlying coding and learning of cue reliability remain uncertain. While much of this work has focused on adjustments in response to rapid changes of reliability, little work has assessed neurophysiological mechanisms to adapt to more stable patterns of reliability. It is also uncertain how the brain learns these stimulus statistics, especially on developmental or evolutionary time scales. This thesis seeks to provide evidence for these capabilities in the barn owl, a model organism for the neural basis of sound localization. The owl uses the interaural time difference (ITD) to compute the azimuth of a sound, with neurons in the auditory midbrain mapped topographically by their tuning to ITD. Previous theoretical work suggested that the barn owl uses a population vector readout of the auditory midbrain map to compute sound location, which can implement Bayesian Inference and explain behavioral biases. In this Bayesian model, the prior is represented by the nonuniform representation of spatial location in the midbrain map and the likelihood is determined by the population activity of these neurons to a given stimulus. The prior and likelihood are then combined to compute sound location. However, electrophysiological recordings of population activity to support the efficacy of the likelihood were lacking. Using a novel multi-electrode array to record neural activity across the midbrain map, I recorded in vivo responses to sounds at varying signal-to-noise ratios. Subsequent analysis indicated that neural activity was sufficient to match the predictions of a population vector readout. Additionally, a decoder based on a population vector could estimate stimulus ITD on a trial-by-trial basis dependent on noise level. These results support feasibility of a population vector readout implementing statistical inference, where cue reliability, in the form of a sound’s location, is built into the architecture of the midbrain map. Subsequent work of this thesis interrogated how anticipated cue reliability is learned over development. Previous work found that the reliability of the ITD cue in the presence of current sounds is dependent on location and frequency of the sound, as well as the acoustical properties of the head. Further, this work suggested that a midbrain map neuron’s frequency tuning matches the frequencies that are most reliable for its preferred ITD. In order to directly test the causality of this relationship between ITD reliability and frequency tuning, I assessed changes in ITD reliability after facial ruff removal, which is known to affect the acoustical properties of the head. Acoustical analyses revealed a localized decrease in the reliability of high frequencies at frontal locations. I tested the hypothesis that if barn owl frequency tuning is shaped by ITD reliability, then owls raised without a facial ruff would show a shift in frequency tuning in the midbrain map. Electrophysiology recordings performed in adult ruff-removed owls, as well as juvenile owls before the facial ruff developed, found shifts in frequency tuning predicted by the acoustical model. This demonstrated that frequency tuning is indeed shaped by the experience of ITD reliability. In addition, behavioral work performed in this thesis testing the discrimination thresholds of owls to different frequencies supports this premise, consistent with a previous report on human ITD detection. Taken together, the work of this thesis provides novel evidence that the barn owl’s brain function is actively driven by the reliability of sensory stimuli. This work also highlights the ability of the brain to fix set weights for certain sensory cues based on the reliability experienced. As the assessment of cue reliability is a common strategy across species, these results provide general insights into the properties of sensory systems.
Space-specific neurons in the owl's midbrain form a neural map of auditory space, which supports sound-orienting behavior. Previous work proposed that a population vector (PV) readout of this map, ...implementing statistical inference, predicts the owl's sound localization behavior. This model also predicts the frontal localization bias normally observed and how sound-localizing behavior changes when the signal-to-noise ratio varies, based on the spread of activity across the map. However, the actual distribution of population activity and whether this pattern is consistent with premises of the PV readout model on a trial-by-trial basis remains unknown. To answer these questions, we investigated whether the population response profile across the midbrain map in the optic tectum of the barn owl matches these predictions using
multielectrode array recordings. We found that response profiles of recorded subpopulations are sufficient for estimating the stimulus interaural time difference using responses from single trials. Furthermore, this decoder matches the expected differences in trial-by-trial variability and frontal bias between stimulus conditions of low and high signal-to-noise ratio. These results support the hypothesis that a PV readout of the midbrain map can mediate statistical inference in sound-localizing behavior of barn owls.
While the tuning of single neurons in the owl's midbrain map of auditory space has been considered predictive of the highly specialized sound-localizing behavior of this species, response properties across the population remain largely unknown. For the first time, this study analyzed the spread of population responses across the map using multielectrode recordings and how it changes with signal-to-noise ratio. The observed responses support the hypothesis concerning the ability of a population vector readout to predict biases in orienting behaviors and mediate uncertainty-dependent behavioral commands. The results are of significance for understanding potential mechanisms for the implementation of optimal behavioral commands across species.
