Organization of the nucleus magnocellularis and the nucleus laminaris in the barn owl: Encoding and measuring interaural time differences

Carr, Catherine E.; Boudreau, R. E.

doi:10.1002/cne.903340302

Cited by 107 publications

(112 citation statements)

References 57 publications

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“…Injection of GABA or muscimol reduced the ITD responses of multi- unit potentials in NL. The results reported above provide the ¢rst in vivo physiological evidence for GABA receptor-mediated inhibition in NL and are consistent with the presence of many GABAergic terminals on neurons in the owl's NL (Carr and Boudreau, 1993). This inhibition must be through GABA A receptors, because muscimol is an agonist speci¢c for this receptor type (Chebib and Johnston, 1999).…”

Section: Discussionsupporting

confidence: 75%

“…Histochemical and neuropharmacological studies with slices have provided evidence for the presence of inhibitory transmitters such as GABA in the NL and glycine in the medial superior olive (Code et al, 1989;Code and Churchill, 1991;Carr and Boudreau, 1993;Lachica et al, 1994;Sanes, 1993, 1994;Hyson et al, 1995;Funabiki et al, 1998;Yang et al, 1999). Despite these observations the action of inhibitory transmitters in these nuclei has not been studied in vivo.…”

Section: Discussionmentioning

confidence: 99%

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Manipulation of inhibition in the owl’s nucleus laminaris and its effects on optic tectum neurons

Takahashi

Konishi

2002

Neuroscience

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Manipulation of inhibition in the owl’s nucleus laminaris and its effects on optic tectum neurons

Takahashi

Konishi

2002

Neuroscience

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“…There are two kinds of very low best frequency magnocellularis neuron, a small round cell, like the higher best frequency principal cell but smaller and with more dendrites, and a stellate cell with an oval cell body [58]. Third, the axons that project from nucleus magnocellularis to low best frequency nucleus laminaris are not as thick or straight as the magnocellular axons shown to act as delay lines in the higher best frequency regions [18,58].…”

Section: Phase-locking Below 2 Khzmentioning

confidence: 99%

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

Carr

Köppl

2004

Journal of Physiology-Paris

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In birds and mammals, precisely timed spikes encode the timing of acoustic stimuli, and interaural acoustic disparities propagate to binaural processing centers. The Jeffress model proposes that these projections act as delay lines to innervate an array of coincidence detectors, every element of which has a different relative delay between its ipsilateral and contralateral excitatory inputs. Thus, interaural time difference (ITD) is encoded into the position of the coincidence detector whose delay lines best cancel out the acoustic ITD. Neurons of the avian nucleus laminaris and mammalian MSO phase-lock to both monaural and binaural stimuli but respond maximally when phase-locked spikes from each side arrive simultaneously, i.e. when the difference in the conduction delays compensates for the ITD. McAlpine et al. [Nat. Neurosci. 4 (2001) 396] identified an apparent difference between avian and mammalian ITD coding. In the barn owl, the maximum firing rate appears to encode ITD. This may not be the case for the guinea pig, where the steepest region of the function relating discharge rate to interaural time delay (ITD) is close to midline for all neurons, irrespective of best frequency (BF). These data suggest that low BF ITD sensitivity in the guinea pig is mediated by detection of a change in slope of the ITD function, and not by maximum rate. We review coding of low best frequency ITDs in barn owls and mammals and discuss whether there may be differences in the code used to signal ITD in mammals and birds.

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“…1 A). Each NL neuron receives input from hundreds of NM cells, each of which is connected to from one to four auditory nerve fibers (19). Within the NL, magnocellular axons run almost parallel to each other and run perpendicular to the NL borders.…”

mentioning

confidence: 99%

“…Within the NL, magnocellular axons run almost parallel to each other and run perpendicular to the NL borders. They act as delay lines, interdigitate, and make synapses mainly on somatic spines of the sparsely distributed laminar neurons (13,19). The latter act as coincidence detectors (10,12,13,(20)(21)(22).…”

mentioning

confidence: 99%

Formation of temporal-feature maps by axonal propagation of synaptic learning

Kempter

Leibold

Wagner

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

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Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 s. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre-and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.C omputational maps (1, 2) transform information about the outside world into topographic neuronal representations. They are a key concept in information processing by the nervous system. Many results point to an ontogenetic development that is critically driven by sensory experience (3). Here we focus on the development of maps of exclusively temporal features (4-10), where the time course of stimuli carries relevant information, in contrast to previous studies that have shown the emergence of orderly representations of spatial or spatiotemporal features. A prominent example is the map of interaural time differences (ITDs) that is used for sound localization (11-13).Sound localization is important to the survival of many animal species-in particular, to those that hunt in the dark. ITDs often are used as a spatial cue. Because the range of available ITDs depends on the size of the head, ITDs are normally well below 1 ms; they can be measured with a precision of up to a few microseconds. In this article, we address the question of how temporal information from both ears can be transmitted to a site of comparison where neurons are tuned to ITDs, and how those ITD-tuned neurons can be organized in a map. A first step toward a solution was made by Jeffress (1) as early as 1948. He proposed a scheme (Fig. 1A) that combines axonal delay lines from both ears and neuronal coincidence detectors to convert ITDs into a place code where a spatial pattern of neuronal activity codes the ITD. His scheme has had a profound impact on understanding sound localization. Neurons tuned to ITDs and maps resembling the circuit envisioned by Jeffress have been observed in many animals...

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Organization of the nucleus magnocellularis and the nucleus laminaris in the barn owl: Encoding and measuring interaural time differences

Cited by 107 publications

References 57 publications

Manipulation of inhibition in the owl’s nucleus laminaris and its effects on optic tectum neurons

Manipulation of inhibition in the owl’s nucleus laminaris and its effects on optic tectum neurons

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

Formation of temporal-feature maps by axonal propagation of synaptic learning

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