Spatial cue reliability drives frequency tuning in the barn Owl's midbrain

Cazettes, Fanny; Fischer, Brian J.; Peña, José Luis

doi:10.7554/elife.04854

Cited by 37 publications

(83 citation statements)

References 58 publications

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“…This is in agreement with previous data on low-frequency responses in the barn owl's core of the inferior colliculus [Wagner et al, 2007]. However, an inhomogeneous representation of the ITD has been observed downstream [Cazettes et al, 2014]. A homogeneous distribution is consistent with a Jeffress type representation of the ITD, based on timedelayed inputs.…”

Section: Evidence For Different Types Of Input Delayssupporting

confidence: 82%

“…However, at frequencies below 3 kHz, this place code model is no longer the clearly optimal solution, and below 800 Hz a change to a population code model was predicted. Low-frequency data are scarce for the barn owl [Wagner et al, 2002[Wagner et al, , 2007Carr and Köppl, 2004;Cazettes et al, 2014]. The aim of the present study was to obtain in-vivo recordings from the low-frequency region of the NL to test predictions of optimal coding.…”

Section: Introductionmentioning

confidence: 99%

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In vivo Recordings from Low-Frequency Nucleus Laminaris in the Barn Owl

Palanca-Castan

Köppl²

2015

Brain Behav Evol

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Localization of sound sources relies on 2 main binaural cues: interaural time differences (ITD) and interaural level differences. ITD computing is first carried out in tonotopically organized areas of the brainstem nucleus laminaris (NL) in birds and the medial superior olive (MSO) in mammals. The specific way in which ITD are derived was long assumed to conform to a delay line model in which arrays of systematically arranged cells create a representation of auditory space, with different cells responding maximally to specific ITD. This model conforms in many details to the particular case of the high-frequency regions (above 3 kHz) in the barn owl NL. However, data from recent studies in mammals are not consistent with a delay line model. A new model has been suggested in which neurons are not topographically arranged with respect to ITD and coding occurs through assessment of the overall response of 2 large neuron populations - 1 in each brainstem hemisphere. Currently available data comprise mainly low-frequency (<1,500 Hz) recordings in the case of mammals and higher-frequency recordings in the case of birds. This makes it impossible to distinguish between group-related adaptations and frequency-related adaptations. Here we report the first comprehensive data set from low-frequency NL in the barn owl and compare it to data from other avian and mammalian studies. Our data are consistent with a delay line model, so differences between ITD processing systems are more likely to have originated through divergent evolution of different vertebrate groups.

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Section: Evidence For Different Types Of Input Delayssupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

In vivo Recordings from Low-Frequency Nucleus Laminaris in the Barn Owl

Palanca-Castan

Köppl²

2015

Brain Behav Evol

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“…In these models, referred to as non-uniform population codes, the statistical structure of the environment is encoded in the non-uniform distribution of preferred stimuli and tuning curve shapes across the population. Studies in birds and humans are consistent with this theory (Fischer and Peña, 2011; Girshick et al, 2011; Cazettes et al, 2014). These studies have shown that the statistical relationship between environmental variables (such as image boundaries or the location of a sound source) and the sensory cues used to make inferences about these variables (edge orientation (Girshick et al, 2011) and interaural time difference (ITD) (Shi and Griffiths, 2009; Fischer and Peña, 2011), respectively) could be represented in the non-uniform tuning properties.…”

Section: Introductionmentioning

confidence: 55%

“…We calculated the environmental variability of IPD and ILD over different directions of concurrent noise sources (Cazettes et al, 2014). For each target direction, a white noise stimulus (0.5 – 12 kHz) was convolved with the head-related impulse responses for the left and right ears at the target direction.…”

Section: Methodsmentioning

confidence: 99%

“…Estimating sound direction from ITD and ILD involves several sources of uncertainty. First, the ITD and ILD computed in the brain are corrupted by noise from the environment (Spitzer et al, 2003; Cazettes et al, 2014) and neural computation (Christianson and Peña, 2006; Pecka et al, 2010). Second, ITD and ILD are not uniquely related to a particular direction in space (Brainard et al, 1992; Fischer and Peña, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Optimal nonlinear cue integration for sound localization

Fischer

Peña

2016

J Comput Neurosci

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Integration of multiple sensory cues can improve performance in detection and estimation tasks. There is an open theoretical question of the conditions under which linear or nonlinear cue combination is Bayes-optimal. We demonstrate that a neural population decoded by a population vector requires nonlinear cue combination to approximate Bayesian inference. Specifically, if cues are conditionally independent, multiplicative cue combination is optimal for the population vector. The model was tested on neural and behavioral responses in the barn owl’s sound localization system where space-specific neurons owe their selectivity to multiplicative tuning to sound localization cues interaural phase (IPD) and level (ILD) differences. We found that IPD and ILD cues are approximately conditionally independent. As a result, the multiplicative combination selectivity to IPD and ILD of midbrain space-specific neurons permits a population vector to perform Bayesian cue combination. We further show that this model describes the owl’s localization behavior in azimuth and elevation. This work provides theoretical justification and experimental evidence supporting the optimality of nonlinear cue combination.

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The Neural Representation of Binaural Sound Localization Cues Across Different Subregions of the Chicken's Inferior Colliculus

Aralla,

Pauley,

Köppl

2024

J of Comparative Neurology

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The sound localization behavior of the nocturnally hunting barn owl and its underlying neural computations is a textbook example of neuroethology. Differences in sound timing and level at the two ears are integrated in a series of well‐characterized steps, from brainstem to inferior colliculus (IC), resulting in a topographical neural representation of auditory space. It remains an important question of brain evolution: How is this specialized case derived from a more plesiomorphic pattern? The present study is the first to match physiology and anatomical subregions in the non‐owl avian IC. Single‐unit responses in the chicken IC were tested for selectivity to different frequencies and to the binaural difference cues. Their anatomical origin was reconstructed with the help of electrolytic lesions and immunohistochemical identification of different subregions of the IC, based on previous characterizations in owl and chicken. In contrast to barn owl, there was no distinct differentiation of responses in the different subregions. We found neural topographies for both binaural cues but no evidence for a coherent representation of auditory space. The results are consistent with previous work in pigeon IC and chicken higher‐order midbrain and suggest a plesiomorphic condition of multisensory integration in the midbrain that is dominated by lateral panoramic vision.

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Spatial cue reliability drives frequency tuning in the barn Owl's midbrain

Cited by 37 publications

References 58 publications

In vivo Recordings from Low-Frequency Nucleus Laminaris in the Barn Owl

In vivo Recordings from Low-Frequency Nucleus Laminaris in the Barn Owl

Optimal nonlinear cue integration for sound localization

The Neural Representation of Binaural Sound Localization Cues Across Different Subregions of the Chicken's Inferior Colliculus

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