Coding interaural time differences at low best frequencies in the barn owl

Carr, Catherine; Köppl, Christine

doi:10.1016/j.jphysparis.2004.03.003

Cited by 22 publications

(19 citation statements)

References 126 publications

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“…Moreover, if a slope code were realized, the responses we observed outside the limit were not expected. However, the slope-code model predicts peaks outside the physiological range (see also Carr and Koppl, 2004), as we observed. We argue here that an observation of peaks outside the physiological ITD range is not sufficient for the conclusion that a slope code is implemented.…”

Section: Representation Of Itd In the Barn Owlsupporting

confidence: 82%

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Distribution of Interaural Time Difference in the Barn Owl's Inferior Colliculus in the Low- and High-Frequency Ranges

Wagner¹,

Asadollahi²,

Bremen³

et al. 2007

J. Neurosci.

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Interaural time differences are an important cue for azimuthal sound localization. It is still unclear whether the same neuronal mechanisms underlie the representation in the brain of interaural time difference in different vertebrates and whether these mechanisms are driven by common constraints, such as optimal coding. Current sound localization models may be discriminated by studying the spectral distribution of response peaks in tuning curves that measure the sensitivity to interaural time difference. The sound localization system of the barn owl has been studied intensively, but data that would allow discrimination between currently discussed models are missing from this animal. We have therefore obtained extracellular recordings from the time-sensitive subnuclei of the barn owl's inferior colliculus. Response peaks were broadly scattered over the physiological range of interaural time differences. A change in the representation of the interaural phase differences with frequency was not observed. In some neurons, response peaks fell outside the physiological range of interaural time differences. For a considerable number of neurons, the peak closest to zero interaural time difference was not the behaviorally relevant peak. The data are in best accordance with models suggesting that a place code underlies the representation of interaural time difference. The data from the high-frequency range, but not from the low-frequency range, are consistent with predictions of optimal coding. We speculate that the deviation of the representation of interaural time difference from optimal-coding models in the low-frequency range is attributable to the diminished importance of low frequencies for catching prey in this species.

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Section: Representation Of Itd In the Barn Owlsupporting

confidence: 82%

“…Most data in the barn owl have been obtained from the highfrequency range (Ͼ2-3 kHz), and not enough data are available from the low-frequency range Carr and Koppl, 2004). The aim of this study is to provide more data to allow discrimination between the two specific model types mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Distribution of Interaural Time Difference in the Barn Owl's Inferior Colliculus in the Low- and High-Frequency Ranges

Wagner¹,

Asadollahi²,

Bremen³

et al. 2007

J. Neurosci.

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show abstract

“…Animal husbandry and experimental protocols were approved by the Animal Care and Use Committee of the University of Maryland, the Regierung von Oberbayern (Germany), the University of Sydney Animal Ethics Committee, and/or the Marine Biological Laboratory (Woods Hole, MA, USA). Detailed procedures for surgery, stereotaxis, acoustic stimulus generation, and data collection have been provided by Carr and Köppl (2004) for owls, Köppl and Carr (2008) for chicks, and Carr et al (2009) for alligators. In brief, animals were anesthetized and placed in a sound-attenuating chamber.…”

Section: Methodsmentioning

confidence: 99%

Effect of sampling frequency on the measurement of phase‑locked action potentials

Ashida¹

2010

Front. Neurosci.

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Phase-locked spikes in various types of neurons encode temporal information. To quantify the degree of phase-locking, the metric called vector strength (VS) has been most widely used. Since VS is derived from spike timing information, error in measurement of spike occurrence should result in errors in VS calculation. In electrophysiological experiments, the timing of an action potential is detected with finite temporal precision, which is determined by the sampling frequency. In order to evaluate the effects of the sampling frequency on the measurement of VS, we derive theoretical upper and lower bounds of VS from spikes collected with finite sampling rates. We next estimate errors in VS assuming random sampling effects, and show that our theoretical calculation agrees with data from electrophysiological recordings in vivo. Our results provide a practical guide for choosing the appropriate sampling frequency in measuring VS.

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“…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%

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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Coding interaural time differences at low best frequencies in the barn owl

Cited by 22 publications

References 126 publications

Distribution of Interaural Time Difference in the Barn Owl's Inferior Colliculus in the Low- and High-Frequency Ranges

Distribution of Interaural Time Difference in the Barn Owl's Inferior Colliculus in the Low- and High-Frequency Ranges

Effect of sampling frequency on the measurement of phase‑locked action potentials

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

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