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Curr Opin Neurobiol. Author manuscript; available in PMC 2012 October 1.
Published in final edited form as:
PMCID: PMC3192259
NIHMSID: NIHMS305116

Sound localization: Jeffress and beyond

Abstract

Many animals use the interaural time differences (ITDs) to locate the source of low frequency sounds. The place coding theory proposed by Jeffress has long been a dominant model to account for the neural mechanisms of ITD detection. Recent research, however, suggests a wider range of strategies for ITD coding in the binaural auditory brainstem. We discuss how ITD is coded in avian, mammalian, and reptilian nervous systems, and review underlying synaptic and cellular properties that enable precise temporal computation. The latest advances in recording and analysis techniques provide powerful tools for both overcoming and utilizing the large field potentials in these nuclei.

The Jeffress model, its variants and alternatives

How can an animal tell the direction a sound is coming from? In 1948, American psychologist Lloyd Jeffress published a germinal paper [1], in which he proposed that the time difference of low frequency sounds arriving at the two ears (interaural time difference, ITD) can be represented as a “place” in an array of nerve cells. The place theory (hereafter also referred to as the Jeffress model) depends on three fundamental assumptions: (1) orderly arrangement in conduction times of ascending nerve fibers, which serve as “delay lines”, (2) conversion of input synchrony into output spike rates by “coincidence detectors”, and (3) systematic variation in spiking rates within the cell array to form a neuronal “place map”. It was only after his death that the first reports appeared, demonstrating anatomically and physiologically the presence of the neuronal ITD maps in the barn owl [2,3]. In contrast to the success in the owl, however, two decades of research in mammals and reptiles have concluded that “Jeffress is not the only answer” for sound localization. In this short review, we look first at various ITD coding schemes, then discuss their underlying synaptic and cellular properties, and briefly review recent advances in recording and analysis techniques.

Birds

Chickens and owls are the most common birds used for the study of neuronal ITD coding. In these species, axons from the nucleus magnocellularis (NM) provide the delay lines, while the neurons in the nucleus laminaris (NL) serve as coincidence detectors and change their spike rates periodically with ITD (Fig. 1A, B). In chickens, NL is a monolayer structure with cells tonotopically arranged mostly along the rostrocaudal axis. Within each single frequency band, the best ITD of the cell (i.e., the ITD to which the spike rate of the cell is maximal) gradually changes along the mediolateral positions [4], therefore forming a single ITD map (Fig. 1A). A three-dimensional reconstruction of the chick NM fibers revealed that both axonal diameters and internodal distances, as well as the axonal length, play an fundamental role in creating the proper neural delays [5*]. In contrast to chickens, owls NL neurons are sparsely distributed in the nucleus without forming a layered structure, resulting in multiple ITD maps in the dorsoventral dimension ([3] and Fig. 1B). Anatomical and in vitro physiological evidence suggests that the emu also has a mono-layered place map in NL [6]. It is still unknown, however, whether the chicken-like single ITD map is prevalent among the bird species.

Figure 1
Various ITD coding strategies

Mammals

In contrast to birds, the existence of ITD maps in mammals has been controversial [710]. Neurons in the medial superior olivary nucleus (MSO) change their spike rates in an ITD-dependent manner, but the peaks of the ITD-rate curves often lie outside the physiologically relevant time range (Fig. 1C; see [9] for more detailed discussion). Moreover, most MSO cells in each hemisphere show similar ITD tuning, which suggests that the average spike rate of many MSO cells codes ITDs, using the “slope” rather than the “peak” of the tuning curves [11]. In the slope coding framework, unlike the place codes found in birds (Fig. 1AB), sounds coming from the contralateral and ipsilateral sides, respectively, result in higher and lower average spiking rates of MSO neurons (Fig. 1C). Note that this slope coding theory is mostly based on the anatomical and physiological results in gerbils. Since recording from the MSO is highly challenging (we will discuss it later), only a limited amount of direct data in other species are available. Findings in guinea pigs seem to be in line with the slope coding scheme [9]. Recent re-examination of the axons from the ventral cochlear nucleus (VCN) to the MSO in cats, however, confirmed a delay-line-like structure, which is compatible with the Jeffress-type model [10]. More evidence would be necessary to conclude to what extent the gerbil-like slope-coding scheme is valid among various mammalian species.

Reptiles

In addition to birds and mammals, reptiles have also been studied recently. In the alligator, bitufted NL neurons are tonotopically arranged in a compact layer, and change their spike rates periodically with ITD, in line with the Jeffress-type model observed in chickens and consistent with their phylogenetic position [12]. In geckos, however, ITD sensitivity appears in the auditory nerve, where there is no binaural neural convergence [13*]. Instead, geckos have pressure gradient receiver ears, and the vibration amplitude of the two eardrums, internally coupled through the mouth cavity, varies with ITD [14,15]. Consequently, firing rates in the gecko auditory nerve change in an ITD-dependent manner (Fig. 1D), providing yet another strategy for ITD detection. How this ITD dependent activity of auditory nerves affects the central nervous system is still under investigation. Binaural comparisons are still necessary in geckos, since monaural directional responses are ambiguous with respect to level and location.

Synaptic and cellular properties

In spite of their different ITD coding strategies, the avian and mammalian auditory brainstems have a great deal in common, including very similar synaptic [16] and cellular [17] mechanisms. This convergence in functional organization reveals basic design features in species that possess unique evolutionary histories but use similar algorithms to solve basic computational problems [18].

Glutamatergic excitation

Excitatory synaptic input in the auditory brainstem, which is primarily mediated by AMPA receptors [19,20], is one of the fastest transmission in the central nervous system. The half peak width of an EPSC is usually below 1 ms [21,22,23**,]. The time scales (rise times and half peak widths) of the glutamatergic excitatory inputs in the chicken NL vary along the tonotopic axis [2022], presumably optimizing the synaptic filtering effect [22]. High frequency NL neurons tend to have faster input time scales than low frequency neurons, enabling transmission of faster signals [2022]. In the gerbil MSO, the excitatory synaptic input originating from the contralateral ear shows faster rise time than that from the ipsilateral ear, partially compensating the longer conduction time through contralateral axons [24**]. A recent study on the gerbil MSO [23**] found that an MSO neuron receives a very small number of excitatory inputs (estimated as 4–8 per cell), in contrast to the owl NL (100–300 afferents per cell)[25]. How these differences in the numbers of excitatory inputs are related to ITD coding remains to be a subject of future studies.

Glycinergic inhibition

Glycinergic inhibition has been shown to play a fundamental role in coding ITDs in the gerbil MSO [26,27]. The bipolar shaped MSO neuron [28] gradually confines glycinergic receptors solely to the somatic area in an experience dependent manner [29]. The number of inhibitory inputs is estimated as 2–4 per cell [23**]. The glycinergic inhibition has a time scale of about 1 msec [30], only slightly slower than the exceptionally fast excitatory input [23**]. This fast inhibition, which is assumed to arrive slightly earlier than the contralateral excitatory input, has been proposed to shift the ITD tuning curve such that the steepest slope of the curve lies within the physiological ITD range (Fig. 1C and [26,27,31]). The balance of excitatory and inhibitory inputs in gerbil MSO, showing similar strength and short term synaptic depression, is thought to be crucial for the slope coding scheme [23**]. Unlike gerbil’s MSO, chicken’s NL (and NM) lacks glycinergic synaptic currents, although NL neurons do coexpress GABA and glycine [32**]. Further investigation is necessary to determine whether (and how, if any) glycinergic activity affects ITD detection in birds.

GABAergic inhibition

Whereas the fast glycinergic inhibition directly affects the position of the ITD tuning curve of the gerbil MSO neuron, effects of slower GABAergic inhibition in the chicken appear more subtle and indirect. GABAergic inhibition has been suggested to improve phase-locking in NM and ITD tuning in NL, by reducing the membrane time constant, shunting and sharpening excitatory synaptic input, counteracting the increase in the input rate due to increasing sound intensity, and balancing ipsi- and contralateral input strengths [3338]. One of the most intriguing characteristics of the GABAergic inhibition is its depolarizing nature even in mature NM and NL cells [3841]. A recent study combining experiment and modeling suggested that the depolarizing inhibition narrows the “coincidence detection time window” of the cell by recruiting low-voltage-activated potassium current and inactivating sodium channels [42].

The source of the GABAergic inhibition to the avian NM and NL is the superior olivary nucleus (SON)[34]. A cautionary note - do not confuse the avian SON with the mammalian MSO - although they share the name, they have different roles and may not be homologous. SON neurons in vivo are broadly tuned to frequency, show several response types, and phase-lock to low frequency tones [43**]. More interestingly, SON neurons receive both GABA- and glycine-mediated inputs [43**]. How these detailed response properties of SON are related to ITD coding in NL remains to be investigated.

Low voltage activated potassium channels

Neurons in the auditory brainstem express a variety of voltage-gated potassium channels [44]. The low-voltage-activated potassium (KLVA) conductance mediated by the Kv1 family is prominent in the ITD coding circuit, and accounts for the robust onset spiking to constant current injection, or the so-called class 3 excitability [45,46]. The KLVA conductance, activated at the resting membrane potential, reduces the input resistance and the membrane time constant, and consequently accelerates the membrane response to facilitate temporal processing in NL and MSO neurons. In addition to this passive property, recent studies have focused on how active properties of the KLVA conductance dynamically affect ITD coding [24**,47,48,49*]. Unlike the conventional method of pharmacological blockade of the KLVA current, these studies use MSO models to virtually “freeze” the KLVA conductance to examine its dynamical effects. In their “frozen KLVA” model, the KLVA conductance is fixed to the resting level, as if it were a constant leak, while in the “active KLVA” model, the KLVA conductance dynamically modulates in a voltage-dependent manner. Model neurons with an active KLVA conductance are more sensitive to rapidly varying input [47] and to the rising phase of slowly varying input with the existence of noise [48]. In the gerbil MSO, active KLVA conductance compensates the distortion of the synaptic input due to dendritic cable filtering [49*], and underlies the selectivity to the temporal ordering of asymmetric excitatory inputs [24**].

Fast sodium channels

In addition to the KLVA channels, fast sodium channels have also received increasing attention, now that improved techniques for recording fast conductance changes have become available (see [50], for discussion on the effect of series resistance compensation on the measurement of fast EPSCs). Somatically recorded action potentials in MSO and NL neurons are exceptionally small (typically 10–20 mV) indicating axonal spike initiation in these neurons [51,52]. Separating the sites of synaptic integration and spike generation is advantageous both computationally and metabolically [53] and enables high frequency firing [54]. In the gerbil MSO, sodium channels, which are distributed in the perisomatic and axonal regions but not in dendrites, are mostly inactivated around the resting potential [55*]. Modeling results indicated, however, that the remaining active channels could amplify subthreshold EPSPs [55*]. Distribution of the sodium channels in chick NL have been shown to be regulated by presynaptic activity [56].

Recordings from MSO and NL in vivo

Sound-induced extracellular field potentials are commonly found in many auditory stations in various animals. This field potential is termed the “neurophonic” since it replicates the waveform of the stimulus tone (see [57] for a review). In NL and MSO, the amplitude of the neurophonic often lies in the millivolt range, hiding small somatic spikes (discussed above) in the background. This makes extracellular single unit recording in these nuclei particularly difficult [58]. There are two types of approach to the neurophonic potential.

Neurophonic

The first approach is to carefully characterize it, in order to extract information about the underlying neural activity. In the barn owl’s NL, the neurophonic potential is temporally very precise and robust [59,60]. The neurophonic amplitude depends on ITD, and its peak position shifts along the ITD map in the nucleus [61]. Therefore, like other local field potentials [62], the neurophonic is presumed to reflect information processing in the nucleus. The biggest problem, however, is the unknown origin of the neurophonic. In the cat MSO [58] and chicken NL [4], where cells are aligned in a thin flat structure, synaptic inputs to the bipolar cells are assumed to be the primary source of neurophonic. In the barn owl, however, oval-shaped NL cells have only short stubby dendrites, and are sparsely distributed in the nucleus [57]. Signal-to-noise ratio analyses and theoretical modeling suggested that the neurophonic in owls NL should originate from either presynaptic NM axons or their excitatory synaptic input to NL, or both [57]. Further investigation is required to identify the source of the neurophonic in owl’s NL.

It has been suggested that field potentials may affect spike timing via ephaptic coupling [63,64]. These studies indicate that hypersynchronized activity of neurons in hippocampus and cortex could strongly entrain neural firing, increasing spike field coherence. If this is also the case for the much faster oscillation in the auditory brainstem nuclei, characterizing the neurophonic potential will be of increasing importance in understanding ITD computations.

Advanced recording techniques

The second approach to the neurophonic is to overcome it using advanced recording procedures. Recent progress in patch-clamp techniques has enabled in vivo whole-cell recordings from the inferior colliculus in mice [65-67], rats [65], and bats [68], and the medial nucleus of the trapezoid body (NMTB) in mice [69], to directly examine subthreshold membrane responses and spike generation mechanisms. The loose-patch/juxtacellular recording techniques [70], which enables hours of stable “quasi-intracellular” recording, has also been used to characterize synaptic transmission in the mouse NMTB [71] and gerbil VCN [72]. Preliminary in vivo recording results that characterize synaptic inputs of owl’s NL (K. Funabiki & M. Konishi, 2005, Assoc Res Otolaryngol Abstr #116) and gerbil’s MSO (M. van der Hijden et al., 2011, Assoc Res Otolaryngol Abstr #695) have also appeared as conference abstracts.

Concluding remarks

Beginning with the Jeffress model, recent studies of sound localization have revealed the presence of multiple ITD coding strategies in birds and mammals. Notwithstanding these differences, all ITD coding depends on the accurate representation of temporal information, which is mediated by similar or identical synaptic and cellular properties. Furthermore, the different ITD coding strategies are not always mutually exclusive; for example, owls may use slopes of the ITD tuning curves in the inferior colliculus (see [73*] for a review).

More evidence is necessary to conclude if there is a unifying ITD computing strategy in mammals. Temporal coding in the ventral cochlear nucleus is very similar among different mammals (e.g., dogs [74], monkeys [75]), but not all mammals show refined ITD tuning in the MSO. Behavioral assessment shows that rats are unlikely to use ITDs [76*], in line with the poorly developed structure of their MSO [29]. Thus, temporal coding is consistent and fairly invariant among species, but may not always be used for computation of ITD. Recent advances in recording and analysis techniques should provide powerful tools for future comparative studies on how NL and MSO compute ITD cues for sound localization.

Highlights

  • New theories have emerged to describe neural coding of interaural time differences.
  • Birds, mammals, and lizards use various coding strategies for locating sound.
  • Nevertheless, the underlying synaptic and cellular properties are very similar.
  • Advanced techniques enable overcoming and utilizing the large field potentials.

Acknowledgments

This work was supported by NIH DC00436 to CEC, NIH P30 DC04664 to the University of Maryland Center for the Evolutionary Biology of Hearing. The authors thank Katrina MacLeod for comments on the manuscript, Yukiyo Nakayama-Ashida for comments on the figure.

Abbreviations

GABA
gamma-aminobutyric acid
ITD
interaural time difference
KLVA
low voltage activated potassium
MNTB
medial nucleus of the trapezoid body
MSO
medial superior olive
NL
nucleus laminaris
NM
nucleus magnocellularis
SON
superior olivary nucleus
VCN
ventral cochlear nucleus

Footnotes

Conflict of interest statement

The authors declare that they have no conflict of interest.

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References

[*] of special interest

[**] of outstanding interest

1. Jeffress LA. A place theory of sound localization. J Comp Physiol Psychol. 1948;41:35–39. [PubMed]
2. Carr CE, Konishi M. Axonal delay lines for time measurement in the owl’s brainstem. Proc Natl Acad Sci USA. 1988;85:8311–8315. [PubMed]
3. Carr CE, Konishi M. A circuit for detection of interaural time differences in the brain stem of the barn owl. J Neurosci. 1990;10:3227–3246. [PubMed]
4. Köppl C, Carr CE. Maps of interaural time difference in the chicken’s brainstem nucleus laminaris. Biol Cybern. 2008;98:541–559. [PMC free article] [PubMed]
5** Seidl AH, Rubel EW, Harris DM. Mechanisms for adjusting interaural time differences to achieve binaural coincidence detection. J Neurosci. 2010;30:70–80. The authors three-dimensionally reconstructed the auditory brainstem nuclei (NM and NL) of the chicken. Their 3-D tracing of NM axons revealed that not only the axonal lengths but also axon diameters and internodal distances should play an essential role in forming maps of ITD in the chicken’s NL. [PMC free article] [PubMed]
6. MacLeod KM, Soares D, Carr CE. Interaural time difference circuits in the auditory brainstem of the emu (Dromaius novaehollandiae) J Comp Neurol. 2006;495:185–201. [PMC free article] [PubMed]
7. Grothe B. New roles for synaptic inhibition in sound localization. Nature Rev Neurosci. 2003;4:1–11. [PubMed]
8. Joris P, Yin TCT. A matter of time: internal delays in binaural processing. Trends Neurosci. 2007;30:70–78. [PubMed]
9. Grothe B, Pecka M, McAlpine D. Mechanisms of sound localization in mammals. Physiol Rev. 2010;90:983–1012. [PubMed]
10. Karino S, Smith PH, Yin TCT, Joris PX. Axonal branching patterns as source of delay in the mammalian auditory brainstem: a re-examination. J Neurosci. 2011;31:3016–3031. [PMC free article] [PubMed]
11. Lesica NA, Lingner A, Grothe B. Population coding of interaural time differences in gerbils and barn owls. J Neurosci. 2010;30:11696–11702. [PubMed]
12. Carr CE, Soares D, Smolders J, Simon JZ. Detection of interaural time differences in the alligator. J Neurosci. 2009;29:7978–7990. [PMC free article] [PubMed]
13* Christensen-Dalsgaard J, Tang Y, Carr CE. Binaural processing by the gecko auditory periphery. J Neurophysiol. 2011 in press. The authors measured the movement of gecko eardrums by laser vibrometry, and recorded from geckos’ auditory nerve in vivo. Both the amplitude of eardrums’ movement and the spike rates of auditory nerve fibers changed in an ITD-dependent manner, showing that the internal coupling of the two ears creates sensitivity to ITD. [PubMed]
14. Vossen C, Christensen-Dalsgaard J, van Hemmen JL. Analytical model of internally coupled ears. J Acoust Soc Am. 2010;128:909–918. [PubMed]
15. Christensen-Dalsgaard J. Vertebrate pressure-gradient receivers. Hear Res. 2011;273:37–45. [PubMed]
16. Trussell LO. Synaptic mechanisms for coding timing in auditory neurons. Ann Rev Physiol. 1999;61:477–496. [PubMed]
17. Trussell LO. Cellular mechanisms for preservation of timing in central auditory pathways. Curr Opin Neurobiol. 1997;7:487–492. [PubMed]
18. Nishikawa KC. Evolutionary convergence in nervous system: insights from comparative phylogenic studies. Brain Behav Evol. 2002;59:240–249. [PubMed]
19. Lu T, Trussell LO. Development and elimination of endbulb synapses in the chick cochlear nucleus. J Neurosci. 2007;27:808–817. [PubMed]
20. Sanchez JT, Wang Y, Rubel EW, Barria A. Development of glutamatergic synaptic transmission in binaural auditory neurons. J Neurophysiol. 2010;104:1774–1789. [PubMed]
21. Kuba H, Yamada R, Fukui I, Ohmori H. Tonotopic specialization of auditory coincidence detection in nucleus laminaris of the chick. J Neurosci. 2005;25:1924–1934. [PubMed]
22. Slee SJ, Higgs MH, Fairhall AL, Spain WJ. Tonotopic tuning in a sound localization circuit. J Neurophysiol. 2010;103:2857–2875. [PubMed]
23** Couchman K, Grothe B, Felmy F. Medial superior olivary neurons receive surprisingly few excitatory and inhibitory inputs with balanced strength and short-term dynamics. J Neurosci. 2010;30:17111–17121. Combining in vitro slice recording and immunohistochemical staining of gerbil MSO neurons, the authors found that MSO neurons receive only few number of excitatory and inhibitory inputs. These inputs were balanced in overall strength and short-term plasticity. These results may necessitate reconsideration of MSO models, which often assume many synaptic inputs. [PubMed]
24** Jercog PE, Svirskis G, Kotak VC, Sanes DH, Rinzel J. Asymmetric excitatory synaptic dynamics underlie interaural time difference processing in the auditory system. PLoS Biol. 2010;8:e1000406. The authors measured excitatory synaptic inputs in the gerbil MSO in vitro, and showed that evoked inputs from the ipsilateral side have a faster rise time than contralateral inputs. This asymmetry may contribute to ITD coding by ensuring that the ITD response curves lie in the physiological relevant range. Their simulation results suggested that the active property of the KLVA conductance plays an important role in detecting the order of slower and faster synaptic inputs. [PMC free article] [PubMed]
25. Carr CE, Boudreau RE. Organization of the nucleus magnocellularis and the nucleus laminaris in the barn owl: encoding and measuring interaural time differences. J Comp Neurol. 1993;334:337–355. [PubMed]
26. Brand A, Behrend O, Marquardt T, McAlpine D, Grothe B. Precise inhibition is essential for microsecond interaural time difference coding. Nature. 2002;417:543–547. [PubMed]
27. Pecka M, Brand A, Behrend O, Grothe B. Interaural time difference processing in the mammalian medial superior olive: the role of glycinergic inhibition. J Neurosci. 2008;28:6914–6925. [PubMed]
28. Rautenberg PL, Grothe B, Felmy F. Quantification of the three-dimensional morphology of coincidence detector neurons in the medial superior olive of gerbils during late postnatal development. J Comp Neurol. 2009;517:385–396. [PubMed]
29. Kapfer C, Seidl AH, Schweizer H, Grothe B. Experience-dependent refinement of inhibitory inputs to auditory coincidence-detector neurons. Nature Neurosci. 2002;5:247–253. [PubMed]
30. Magnusson AK, Kapfer C, Grothe B, Koch U. Maturation of glycinergic inhibition in the gerbil medial superior olive after hearing onset. J Physiol. 2005;568:497–512. [PubMed]
31. Leibold C. Influence of inhibitory synaptic kinetics on the interaural time difference sensitivity in a linear model of binaural coincidence detection. J Acoust Soc Am. 2010;127:931–942. [PubMed]
32** Kuo SP, Bradley LA, Trussell LO. Heterogeneous kinetics and pharmacology of synaptic inhibition in the chick auditory brainstem. J Neurosci. 2009;29:9625–9634. These authors compared inhibitory synaptic inputs in NM, NL, and NA (nucleus angularis, which is a part of sound intensity coding circuit) using chicken brainstem slice recordings. Although all these nuclei commonly receive inhibitory inputs from the SON, IPSCs in NM were considerably slower than those of NL and NA. Moreover, IPSCs in NA are mediated by both GABA and glycine, while those in NM and NL lack glycinergic components. [PMC free article] [PubMed]
33. Peña JL, Viete S, Albeck Y, Konishi M. Tolerance to sound intensity of binaural coincidence detection in the nucleus laminaris of the owl. J Neurosci. 1996;16:7046–7054. [PubMed]
34. Burger RM, Cramer KS, Pfeiffer JD, Rubel EW. Avian superior olivary nucleus provides divergent inhibitory input to parallel auditory pathways. J Comp Neurol. 2005;481:6–18. [PubMed]
35. Dasika VK, White JA, Carney LH, Colburn HS. Effects of inhibitory feedback in a network model of avian brain stem. J Neurophysiol. 2005;94:400–414. [PubMed]
36. Nishino E, Yamada R, Kuba H, Furuta T, Kaneko T, Ohmori H. Sound-intensity-dependent compensation for the small interaural time difference cue for sound source localization. J Neurosci. 2008;28:7153–7164. [PubMed]
37. Fukui I, Burger RM, Ohmori H, Rubel EW. GABAergic inhibition sharpens the frequency tuning and enhances phase locking in chicken nucleus magnocellularis neurons. J Neurosci. 2010;30:12075–12083. [PMC free article] [PubMed]
38. Burger RM, Fukui I, Ohmori H, Rubel EW. Inhibition in the balance: binaurally coupled inhibitory feedback in sound localization circuitry. J Neurophysiol. 2011 in press. [PubMed]
39. Lu T, Trussell LO. Mixed excitatory and inhibitory GABA-mediated transmission in chick cochlear nucleus. J Physiol. 2001;535:125–131. [PubMed]
40. Monsivais P, Rubel EW. Accomodation enhances depolarizing inhibition in central neurons. J Neurosci. 2001;21:7823–7830. [PubMed]
41. Tang Z-Q, Gao H, Lu Y. Control of a depolarizing GABAergic input in an auditory coincidence detection circuit. J Neurophysiol. 2009;102:1672–1683. [PubMed]
42. Howard MA, Rubel EW. Dynamic spike thresholds during synaptic integration preserve and enhance temporal response properties in the avian cochlear nucleus. J Neurosci. 2010;30:12063–12074. [PMC free article] [PubMed]
43** Coleman WL, Fischl MJ, Weimann SR, Burger RM. GABAergic and glycinergic inhibition modulate monaural auditory response properties in the avian superior olivary nucleus. J Neurophysiol. 2011 in press. The authors recorded in vivo from the chicken SON, which is the source of the GABAergic inhibition to NM and NL. They reported three response types (sustained, onset and suppressed), with broad frequency tuning, and phase-locking to low frequency tones. They also showed that SON neurons receive both GABA- and glycine-mediated inhibition. [PubMed]
44. Johnston J, Forsythe ID, Kopp-Scheinpflug C. Going native: voltage-gated potassium channels controlling neuronal excitability. J Physiol. 2010;588:3187–3200. [PubMed]
45. Clay JR, Paydarfar D, Forger DB. A simple modification of the Hodgkin and Huxley equations explains type 3 excitability in squid giant axons. J R Soc Interface. 2008;5:1421–1428. [PMC free article] [PubMed]
46. Prescott SA, De Koninck Y, Sejnowski TJ. Biophysical basis for three distinct dynamical mechanisms of action potential initiation. PLoS Comput Biol. 2008;4:e1000198. [PMC free article] [PubMed]
47. Day ML, Doiron B, Rinzel J. Subthreshold K+ channel dynamics interact with stimulus spectrum to influence temporal coding in an auditory brain stem model. J Neurophysiol. 2008;99:534–544. [PubMed]
48. Gai Y, Doiron B, Kotak V, Rinzel J. Noise-gated encoding of slow inputs by auditory brain stem neurons with a low-threshold K+ current. J Neurophysiol. 2009;102:3447–3460. [PubMed]
49* Mathews PJ, Jercog PE, Rinzel J, Scott LL, Golding NL. Control of submillisecond synaptic timing in binaural coincidence detectors by KV1 channels. Nature Neurosci. 2010;13:601–611. The authors performed paired recordings from the soma and a dendrite of the bipolar-shaped gerbil MSO neuron in vitro, and found voltage-dependent sharpening of EPSPs. Their simulation results showed that non-uniform distribution and active properties of KLVA channels may be important in sharpening EPSPs to counteract the dendritic filtering, which can degrade temporal fidelity. [PMC free article] [PubMed]
50. Kuba H, Yamada R, Ohmori H. Evaluation of the limiting acuity of coincidence detection in nucleus laminaris of the chicken. J Physiol. 2003;552:611–620. [PubMed]
51. Scott LL, Mathews PJ, Golding NL. Posthearing developmental refinement of temporal processing in principal neurons of the medial superior olive. J Neurosci. 2005;25:7887–7895. [PubMed]
52. Kuba H, Ishii TM, Ohmori H. Axonal site of spike initiation enhances auditory coincidence detection. Nature. 2006;444:1069–1072. [PubMed]
53. Ashida G, Abe K, Funabiki K, Konishi M. Passive soma facilitates submillisecond coincidence detection in the owl’s auditory system. J Neurophysiol. 2007;97:2267–2282. [PubMed]
54. Scott LL, Hage TA, Golding NL. Weak action potential backpropagation is associated with high-frequency axonal firing capability in principal neurons of the gerbil medial superior olive. J Physiol. 2007;583:647–661. [PubMed]
55* Scott LL, Mathews PJ, Golding NL. Perisomatic voltage-gated sodium channels actively maintain linear synaptic integration in principal neurons of the medial superior olive. J Neurosci. 2010;30:2039–2050. Performing in vitro whole-cell current- and voltage-clamp recordings, these authors found that most sodium channels in gerbil MSO are inactivated at the resting potential because the voltage dependence of the inactivation curve is unusually hyperpolarized. Their simulation suggested that the remaining sodium current in the soma may amplify EPSPs. [PMC free article] [PubMed]
56. Kuba H, Oichi Y, Ohmori H. Presynaptic activity regulates Na+ channel distribution at the axon initial segment. Nature. 2010;465:1075–1078. [PubMed]
57. Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. Theoretical and experimental signal-to-noise ratio of the neurophonic potential in nucleus laminaris of the barn owl (Tyto alba) J Neurophysiol. 2010;104:2274–2290. [PubMed]
58. Mc Laughlin M, Verschooten E, Joris PX. Oscillatory dipoles as a source of phase shifts in field potentials in the auditory brainstem. J Neurosci. 2010;30:13472–13487. [PubMed]
59. Wagner H, Brill S, Kempter R, Carr CE. Microsecond precision of phase delay in the auditory system of the barn owl. J Neurophysiol. 2005;94:1655–1658. [PMC free article] [PubMed]
60. Wagner H, Brill S, Kempter R, Carr CE. Auditory responses in the barn owl’s nucleus laminaris to clicks: impulse response and signal analysis of neurophonic potential. J Neurophysiol. 2009;102:1127–1240. [PubMed]
61. Sullivan WE, Konishi M. Neural map of interaural phase difference in the owl’s brainstem. Proc Natl Acad Sci USA. 1986;83:8400–8404. [PubMed]
62. Rasch M, Logothetis NK, Kreiman G. From neurons to circuits: linear estimation of local field potentials. J Neurosci. 2009;29:13785–13796. [PMC free article] [PubMed]
63. Radman T, Su Y, An JH, Parra LC, Bikson M. Spike timing amplifies the effect of electric fields on neurons: implications for endogenous field effects. J Neurosci. 2007;27:3030–3036. [PubMed]
64. Anastassiou CA, Perin R, Markram H, Koch C. Ephaptic coupling of cortical neurons. Nature Neurosci. 2011;14:217–223. [PubMed]
65. Tan ML, Theeuwes HP, Feenstra L, Borst JGG. Membrane properties and firing patterns of inferior colliculus neurons: an in vivo patch-clamp study in rodents. J Neurophysiol. 2007;98:443–453. [PubMed]
66. Geis H-R, Borst JGG. Intracellular responses of neurons in the mouse inferior colliculus to sinusoidal amplitude-modulated tones. J Neurophysiol. 2009;101:2002–2016. [PubMed]
67. Nagtegaal AP, Borst JGG. In vivo dynamic clamp study of Ih in the mouse inferior colliculus. J Neurophysiol. 2010;104:940–948. [PubMed]
68. Li N, Gittelman JX, Pollak GD. Intracellular recordings reveal novel features of neurons that code interaural intensity disparities in the inferior colliculus. J Neurosci. 2010;30:14573–14584. [PMC free article] [PubMed]
69. Lorteije JAM, Rusu SI, Kushmerick C, Borst JGG. Reliability and precision of the mouse calyx of Held synapse. J Neurosci. 2009;29:13770–13784. [PubMed]
70. Joshi S, Hawken MJ. Loose-patch-juxtacellular recording in vivo--A method for functional characterization and labeling of neurons in macaque V1. J Neurosci Meth. 2006;156:37–49. [PubMed]
71. Lorteije JAM, Borst JGG. Contribution of the mouse calyx of Held synapse to tone adaptation. Eur J Neurosci. 2011;33:251–258. [PubMed]
72. Kuenzel T, Borst JGG, van der Hijden M. Factors controlling the input-output relationship of the spherical bushy cells in the gerbil cochlear nucleus. J Neurosci. 31:4260–4273. [PubMed]
73* Takahashi TT. How the owl tracks its prey – II. J Exp Biol. 2010;213:3399–3408. This paper, having the same title as a review published about 40 years ago (Konishi M, 1973; American Scientist), reviews how studies of barn owls have contributed to our understanding of sound localization, neural plasticity, and audio-visual interaction. [PubMed]
74. Bal R, Baydas G, Naziroglu M. Electrophysiological properties of ventral cochlear nucleus neurons of the dog. Hear Res. 2009;256:93–103. [PubMed]
75. Rhode WS, Roth GL, Recio-Spinoso A. Response properties of cochlear nucleus neurons in monkeys. Hear Res. 2010;259:1–15. [PMC free article] [PubMed]
76* Wesolek CM, Koay G, Heffner RS, Heffner HE. Laboratory rats (Rattus norvegicus) do not use binaural phase differences to localize sound. Hear Res. 2010;265:54–62. The authors evaluated the sound localization ability of laboratory rats. Rats were not able to localize low frequency sounds below 2 kHz, suggesting that they are unlikely to use time difference cues in sound localization. Their results indicate that rats may not be a suitable animal model for investigating ITD computation. [PubMed]
77. Kubke MF, Massoglia DP, Carr CE. Developmental changes underlying the formation of the specialized time coding circuits in barn owls (Tyto alba) J Neurosci. 2002;22:7671–7679. [PMC free article] [PubMed]