J Neurosci. 2021 Nov 10:JN-RM-1061-21. doi: 10.1523/JNEUROSCI.1061-21.2021. Online ahead of print.
ABSTRACT
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 bases remains unknown. To answer these questions, we investigated whether the population response profile across the midbrain map in the barn owl’s optic tectum matches these predictions using in vivo multi-electrode array recordings. We found that response profiles of recorded sub-populations are sufficient for estimating the stimulus ITD 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.SIGNIFICANCE STATEMENTWhile the tuning of single neurons in the owl’s midbrain map of auditory space has been considered predictive of this species’ highly specialized sound localizing behavior, response properties across the population remain largely unknown. For the first time, this study analyzed the spread of population responses across the map using multi-electrode recordings and how it changes with signal-to-noise ratio. The observed responses support the hypothesis of a population vector readout’s ability 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.
PMID:34764158 | DOI:10.1523/JNEUROSCI.1061-21.2021
