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date: 26 May 2019

The Medial Superior Olivary Nucleus: Meeting the Need for Speed

Abstract and Keywords

It doesn’t get any more precise than this: Neurons in the medial superior olive (MSO) in many mammals, including humans, are sensitive to temporal differences between their synaptic inputs of only a few microseconds. These neurons are the basis for our ability to resolve even a 5-μs disparity in the time of arrival of a sound at each ear. This capacity enables humans to discriminate between temporally overlapping sounds based on spatial segregation, as for instance, at a cocktail party or a poster session at a scientific conference. This chapter aims at providing a comprehensive summary on the current state of research regarding the MSO, ranging from cellular and circuit anatomy to subcellular and channel physiology to spatial coding and perception. Consequently, the chapter is subdivided according to these thematic aspects. Nonetheless, while such subdivisions are helpful for providing structure to the reader, they can also convey the inter-dependencies between these topics: for example, when studying the spatial sensitivity of MSO neurons on the mechanistic level, it is crucial to also consider its anatomical specializations (on the subcellular and circuit level), as well as corresponding perceptional phenomena. Finally, the chapter suggests a complete picture of the MSO only emerges by including evolutionary considerations, that is, the phylogenetic origin of the many fascinating specializations that can be observed within the MSO circuit.

Keywords: coincidence detection, ITD, neuronal population code, SPN, adaptation, glycine, spatial hearing, context-dependent sound localization, ultrafast neural processing

Preface: Definition and Computations

Traditionally, neuronal structures are defined as regions or nuclei that can be anatomically delineated and, ideally, unambiguously recognized in all species of a clade (here: in all mammals). Usually, it is irrelevant whether the structure in question contains different neuronal types and/or serves one or more functions. The cochlear nucleus (CN), for instance, contains multiple neuronal types that clearly serve different functions, but these cell types can be identified in all mammals and always permit unambiguous delineation of the CN. In the case of the MSO, matters are more complex, and its delimitation has been guided largely by the morphology of a specific cell type, which is arranged in a particular way and performs an explicit function, rather than by comparative means. Specifically, the textbook definition of the MSO is as follows: A structure that consists of bipolar neurons whose somata are located in one parasagittal plane, whose dendrites are oriented orthogonally to this plane, point medially and laterally, and serve only one function. This “defining” function is the detection of microsecond differences in the time of arrival of inputs from the two ears (interaural time difference, ITD), which allows differences in the bilateral time of arrival of sounds to be utilized for sound localization in the horizontal plane. This straight-forward view of the MSO goes back to early studies in carnivores (e.g., cats and dogs), animals in which the MSO attracted attention because of the sheer beauty of its unique anatomical organization (Goldberg & Brown, 1969; Ramón y Cajal, 1909; Figure 1). Moreover, the MSOs of dogs and cats were among the first shown to be sensitive to ITDs (Caird & Klinke, 1983; Goldberg & Brown, 1969; Yin & Chan, 1990). Although its cytoarchitecture (Kapfer et al., 2002; Rautenberg, Grothe, & Felmy, 2009) and its ITD sensitivity (Brand et al., 2002; Franken, Bremen, & Joris, 2014; Pecka et al., 2008; Spitzer & Semple, 1995; van der Heijden et al., 2013) have since been confirmed in other ITD-using mammals, reducing the MSO to a single neuron type and a single function is problematic for the following reasons:

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Figure 1. The MSO.

The position of the MSO deep in the brainstem is shown in a coronal section (left side). Glycine receptors (yellow) are restricted to the area of the cell bodies of MSO cells, which are arranged in a sagittal plane, with dendrites pointing medially and laterally (blue). All connections except the SPN-MSO projection (see Figure 2) are phase-locked to low-frequency sounds and time-locked to the sound envelope. From Kapfer et al. (2002), with permission.

  1. 1. The MSO includes neurons that deviate from the strictly bipolar type and their unique biophysical properties (Smith, 1995).

  2. 2. Even the predominant bipolar neurons are not always arranged within one parasagittal plane, as they are in dogs, cats, or gerbils. In small mammals that do not use ITDs for sound localization, like various bats (reviewed in Grothe, 2000), mice (Fischl et al., 2016), rats, and short-tailed opossums (Kapfer et al., 2002), these cells are distributed within a more spherical MSO. Indeed, this diversity has given rise to the erroneous claim that the MSO is missing in these mammals.

  3. 3. Synaptic input patterns of MSO bipolar neurons differ across mammals, although they receive the same set of inputs from both sides of the brain (to be discussed in “The MSO Circuit and Anatomical Adaptations for Speed”). In mammals that use ITDs, such as cats, gerbils, and chinchillas, the four major axonal projections to MSO are arranged in an orderly fashion, with ipsilateral glutamatergic inputs targeting only lateral, and contralateral glutamatergic inputs targeting only medial dendrites. In addition, glycinergic inputs from both sides synapse only in the somatic region of these neurons (Clark, 1969; Kapfer et al., 2002; Kuwabara & Zook, 1992; Perkins, 1973; Stotler, 1953; Werthat et al., 2008). In contrast, in mammals that do not exploit ITDs, the inhibitory and excitatory inputs are intermingled along the somata and among the dendrites (Kapfer et al., 2002).

  4. 4. Finally, bipolar neurons in the MSO of bats can be highly sensitive to temporal features of sounds (like amplitude modulations; Grothe, 1994), but there ITD sensitivity seems to be too crude to serve sound localization for these small animals (Grothe & Park, 1998; Harnischfeger, Neuweiler, & Schlegel, 1985) or show predominantly monaural response properties (Covey, Vater, & Casseday, 1991; Grothe et al., 1992).

A phylogenetic framework that can explain these differences may be found in Grothe (2000) and (Grothe & Pecka, 2014). In this chapter, we follow a strategy that avoids problems relating to the anatomical “delineation” of nuclei/structures and simplifies matters, without losing sight of the organizational and functional variations found across different species. This can be done by simply addressing specific cell types with definable functional and/or computational properties independently of their exact location or arrangement within the SOC, rather than focusing on structures that contain multiple neuronal types. Emphasizing cell identity is the only way to disentangle complexities associated with the definition of a nucleus across species, and it may also help clarify the definitions of the superior periolivary/paraolivary nucleus (SPN/SPON); for example, with regard to the OFF cells that have been assigned to different nuclei in different species (compare to chapter by Magnusson & Gómez-Álvarez, this volume; see also Behrend et al., 2002).

With regard to MSO cell types, rather little is known about the properties of the “non-principal” neurons, except that they are multipolar, exhibit low thresholds, and fire repetitively upon current injections in vitro, which contrasts with the high threshold phasic responses seen in bipolar neurons (Smith, 1995). Nothing is known about their responses or function in vivo. We will therefore focus on the principal MSO neurons in ITD-using mammals only. These neurons are tuned for execution of the temporally most precise computations known in mammals, which rely on their anatomical and synaptic arrangement, as well as on some peculiar features of their inputs and their rather unique biophysical properties.

We will address and summarize in turn (1) the anatomical arrangement of principal cells, including synaptic inputs and the morphological adaptations for speed; (2) their biophysical properties, which permit processing of information on microsecond scales; (3) the computational tasks involved in ITD processing; and (4) the functional significance of the coding properties of these cells and their implications for auditory spatial perception.

The MSO Circuit and Anatomical Adaptations for Speed

A major feature of the MSO and its input pathways in mammals that use ITDs (Figure 2) is the structural organization that enables accurate registration of the arrival times of inputs. The specializations begin at the level of the two classes of bushy cells and their inputs (see chapter by Oertel, Cao, & Recio-Spinoza, this volume), which provide precise information relating to the temporal features of stimuli, such as envelope onsets and the fine structure of ongoing components (e.g., phase-locking to low-frequency sounds). Spherical bushy cells (SBCs) on both sides provide the binaural excitatory, glutamatergic inputs to the MSO, with ipsilateral inputs targeting the lateral and contralateral inputs the medial dendrites. Hence, as first described in detail by Stotler (1953), ipsi- and contralateral inputs are kept spatially separated at the level of the MSO neurons. It follows that owing to the longer path length, excitation from the contralateral side arrives slightly later than that from the ipsilateral ear. Because spike generation by the MSO neurons depends on coincidence detection (see under the following heading, “Biophysical Properties”), this configuration of inputs may itself generate a “preferred ITD”; that is, provide an external delay between incoming signals from the two ears that precisely offsets the difference in axonal path length between the excitatory inputs from the two sides. This “preferred delay” corresponds to an ITD that favors sounds from the contralateral hemisphere, since slightly earlier arrival of the sound at the contralateral ear will compensate for the internal delay. Indeed, this slight contralateral bias has been experimentally observed in the MSO (Brand et al., 2002; Franken, Bremen, & Joris, 2014; Pecka et al., 2008; to be discussed in “The Nature of Internal Delays” and Figure 6c).

In contrast to its avian analogue the nucleus laminaris (Seidl, Rubel, & Harris, 2010), in which the excitatory inputs are arranged as delay lines that provide different combinations of internal delays to different binaural neurons (in accordance with the scenario proposed by L. Jeffress [Jeffress, 1948]), there is no evidence for systematic variation in axonal length in the MSO inputs (Grothe, 2000; Karino et al., 2011).

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Figure 2. The MSO circuit.

MSO neurons receive excitatory inputs from SBCs in both the ipsi- and contralateral VCN. SBC fibers target MSO dendrites. Bushy cells are driven by the auditory nerve. GBCs in the ipsilateral VCN excite ipsilateral LNTB neurons via large endbulb-of-Held-like synapses. GBCs from the contralateral side make contact with ipsilateral MNTB neurons (MNTB) via the calyx of Held synapse. GBC fibers projecting to low-frequency MNTB neurons have large axon diameters but unusually short internodes. SBC fibers are much thinner but have longer internodes. MNTB and LNTB neurons are glycinergic (inhibitory) and target MSO cell bodies and proximal dendrites. MSO cells send their main output to the inferior colliculus (auditory midbrain). Collaterals from this projection synapse on GABAergic neurons in the SPN. These SPN neurons provide feedback into the MSO via slow volume transmission targeting presynaptic GABAB receptors.

GBC = globular bushy cell; LNTB = lateral nucleus of the trapezoid body; MNTB = medial nucleus of the trapezoid body; MS0 = medial superior olive; SPN = superior paraolivary nucleus; VCN = ventral cochlear nucleus.

In addition to bilateral excitation, the MSO receives bilateral inhibitory synaptic inputs and at least one additional inhibitory feedback input that acts via volume transmission (Figure 2). The latter originates from a monosynaptic MSO-SPN-MSO feedback loop. We will address the function of this GABAergic volume transmission later on in the section “The Nature of the Spatial Code and Dynamics of Spatial Processing”. The direct synaptic inhibition is glycinergic (Grothe & Sanes 1993, 1994) and comes from the medial and, less prominently, from the lateral nucleus of the trapezoid body (MNTB, LNTB, respectively; (Kuwabara & Zook, 1992; Spirou & Berrebi, 1997; Spirou, Rowland, & Berrebi, 1998). In contrast to the case in other mammals, in which these inputs target both dendrites and somata of MSO neurons, experience-dependent developmental pruning of glycinergic inputs from the dendrites takes place in ITD-using mammals, which restricts large inhibitory synapses to the somata and the most proximal dendrites (Couchman, Grothe, & Felmy, 2012; Kapfer et al., 2002; Werthat et al., 2008; Figure 3). This arrangement correlates with the fact that strong inhibitory currents can be elicited by only a small number of input fibers (Couchman, Grothe, & Felmy, 2010). Perturbation of this developmental removal of dendritic inhibitory inputs (Kapfer et al., 2002) disrupts the normal development of ITD tuning in gerbils (Seidl & Grothe, 2005). How such inhibition may alter ITD tuning will be discussed as well.

The inhibitory pathway starts with globular bushy cells (GBCs), which excite a monaural (ipsilateral) subpopulation of LNTB neurons (Roberts, Seeman, & Golding, 2014) via large endbulb of Held-like synapses (Spirou, Rowland, & Berrebi, 1998) and (contralateral) MNTB neurons via the extensively studied calyx of Held synapse (Borst & Soria van Hoeve, 2012). LNTB and, more prominently, MNTB then provide glycine-mediated hyperpolarizing inhibition to the MSO somata (Grothe & Sanes, 1993; Kapfer et al., 2002; Roberts, Seeman, & Golding, 2014).

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Figure 3. Developmental maturation of MSO anatomy after hearing onset.

(A) Before hearing onset, MSO dendrites are thin and highly branched. The glycinergic inhibitory inputs from MNTB and LNTN are targeted to dendrites and cell somata. (B) One week after hearing onset, the MSO dendrites are thicker and less tufted. The inhibitory inputs are now confined to the cell somata and proximal dendrites.

The GBC-MNTB-MSO pathway in particular exhibits unique adaptations for fast conduction of action potentials and temporally precise transmission. First, GBCs receive strong synaptic inputs from auditory nerve (AN) fibers (Xu-Friedman & Regehr, 2005; see Oertel et al., this volume) that cause action potentials to follow the temporal structure of inputs with the highest possible temporal precision. For instance, the precision of phase-locking in GBCs surpasses that of single AN fibers, as shown for cats and gerbils (Joris et al., 1994; Wei et al., 2017). Second, GBC fibers have the largest axon diameter known in the auditory brainstem (Ford et al., 2015; Morest, 1968), which partly explains their higher conduction velocity. Moreover, GBC axons tuned to low frequencies have recently been shown to exhibit a unique myelination pattern: although they are even thicker than GBCs tuned to higher frequencies (and much thicker than SBC axons), their internodes are significantly shorter. Although at first sight paradoxical, this pattern in fact results in an even higher conduction velocity (Ford et al., 2015; Figure 2, Figure 6B). Notably, this unusual myelination pattern is not found on GBC axons of mice, which do not utilize ITDs; hence, it appears to be specifically related to ITD processing (Stange-Marten et al., 2017). Third, each MNTB neuron receives input from a GBC fiber via a single calyx of Held synapse, a type known for its exceptional speed and reliability (Borst & Soria van Hoeve, 2012). Fourth, as has recently been shown in gerbils, a significant proportion of such synapses tuned to low sound frequencies do not prolong synaptic delays during ongoing activity, a feature that is highly unusual for chemical synapses (Stange-Marten et al., 2017). Hence, the additional synapse not only expedites conduction speed, but in these cases also ensures absolute time constancy despite the additional synapse. Finally, the inputs are restricted to the MSO cell somata (Clark, 1969; Kapfer et al., 2002), which results in precise and strong inhibition (Couchman, Grothe, & Felmy, 2012; Grothe & Sanes, 1994; Roberts, Seeman, & Golding, 2013).

These unusual properties of the main inhibitory pathway to the MSO relate to a number of indirect and direct observations that demonstrate that this inhibition, despite the additional synapse in the MNTB, precedes the direct excitation from contralateral SBGs (Brand et al., 2002; Goldwyn et al., 2017; Grothe & Pecka, 2014; Roberts, Seeman, & Golding, 2013) and acts in a phase-locked manner (Pecka et al., 2008; see also figure 1d in Franken et al., 2015; compare figures 2 and 5 in Goldwyn et al., 2017). Less is known about the weaker LNTB-mediated inhibition, but in vitro recordings suggest that its timing may be less reliable than that of MNTB-driven inhibition (Roberts, Seeman, & Golding, 2014). Since strong inhibitory input is consistently found in in vitro MSO preparations, it is clear that inhibition at least contributes to cellular ITD processing. Its particular role is nonetheless still under debate, as we will discuss. But first we turn to biophysical aspects of cellular coincidence detection of excitatory and inhibitory inputs.

Biophysical Properties

To achieve cycle-by-cycle ITD sensitivity of their phase-locked inputs, MSO neurons must be able to limit temporal integration of synaptic currents to the oscillation period of the fine structure of their bushy cell inputs. For example, if we assume the phase-locking limit of bushy cells to be about 2 kHz, the memory time scale of the coincidence mechanism must be at least 1/(2 kHz) = 500 µs or lower. Such microsecond-scale operation places strong constraints on the biophysical properties of the neural elements involved, including dendritic filtering, synaptic dynamics, and transmembrane channel kinetics. In the following paragraphs, we will review these aspects in the mature MSO cell and consider how they contribute to ITD processing.

Dendrite Morphology and Synaptic Distributions

As mentioned earlier, a defining anatomical property of MSO principal neurons is their bipolar form, with one lateral and one medial dendrite originating from the soma ( Henkel & Brunso-Bechtold, 1990; Kiss & Majorossy, 1983; Rautenberg, Grothe, & Felmy, 2009; Stotler, 1953). The two major dendrites are relatively thick (~4 µm diameter) close to the soma, such that the border between soma and dendrite is not clearly demarcated anatomically, and is largely a matter of definition (Rautenberg, Grothe & Felmy, 2009). The primary dendrites are relatively short (100 to 150 µm) and tapered, terminating in filopodia-like structures. Often one also observes dendrites branching at an intermediate distance from the soma and leading to two major dendritic trunks. The marked bipolar morphology serves two major functional purposes:

  1. 1. Excitatory synaptic inputs from the two input pathways remain anatomically segregated, with inhibitory synapses from either hemisphere being restricted to the soma (Clark, 1969; Kapfer et al., 2002; Kuwabara & Zook, 1992; Stotler, 1953).

  2. 2. Owing to the thickness of the proximal dendrite its axial resistance is very low, and it therefore has little temporal filtering capacity. The dendrites also act as large current sinks, which results in additional leakage from the soma (Agmon-Snir, Carr, & Rinzel, 1998; Dasika, White, & Colburn, 2007; Rall, 1962). The relatively small diameters of the distal dendrites result in locally higher input resistances and thereby amplify voltage responses to distal inputs relative to more proximal inputs (Mathews et al., 2010).

It has been argued that the separation of inputs on dendrites facilitates binaural coincidence detection via a ceiling effect imposed by the synaptic reversal potential (Agmon-Snir, Carr, & Rinzel, 1998; Dasika, White, & Colburn, 2007), which leads to an effective reduction of the threshold conductance for binaural inputs relative to monaural inputs. This threshold effect, however, is particularly strong for thin dendrites and might thus be more relevant to the avian binaural coincidence detector in the nucleus laminaris (Smith & Rubel, 1979) than to the mammalian MSO with its thick dendrites.

Thus, despite the clear anatomical allocation of the binaural input pathways to the lateral and medial dendrites first described by Stotler (1953), its functional role remains unclear. It may have an important developmental function, for example in selecting the composition of inputs that provide precise ipsi- or contralateral excitation/inhibition appropriately tuned to the timing of the other inputs. In this context, a detailed description of how individual fibers make synaptic contacts along the dendrite is of the utmost importance, because this pattern defines the temporal profile of dendritic depolarization upon unilateral excitation. Unfortunately, very little is known about the properties of single fibers. Based on single-fiber stimulation, Couchman et al. (2010) estimated that only 1 or 2 active excitatory SBC fibers per cochlear nucleus are necessary to evoke an action potential from the resting state. However, this result was obtained using single stimuli applied to slices during blockade of inhibitory inputs. When repetitive stimulation (pulse trains of few hundred Hz) is employed under similar conditions, these numbers increase by more than twofold due to synaptic adaptation (N. Kladisios and F. Felmy, personal communication, January 26, 2018). But even the latter figures are surprisingly small, given the large number of excitatory synaptic contacts that have been described anatomically (Stotler, 1953). The available evidence thus suggests that individual fibers may make multiple contacts on one dendrite. An anatomical verification of this assertion is, however, still lacking.

Synaptic Kinetics and Integration

Synaptic transmission at MSO cells mainly relies on two ligand-gated ion channels, amino-3-hydroxyl-5-methyl-4-isoxazole propionate (AMPA) receptor-mediated excitation and glycine receptor-mediated inhibition. Only a relatively small (< 5%) N-methyl-D-aspartate (NMDA) receptor-mediated current has been reported to contribute to EPSCs in the adult MSO (Couchman, Grothe, & Felmy, 2010; Smith, Owens, & Forsythe, 2000). MSO AMPA signaling is spectacularly fast. In contrast to the AMPA decay time constants of sometimes 10 and more milliseconds known from cortical synapses (see e.g., (Salin et al., 1996)), voltage clamp recordings from MSO neurons reveal decay time constants of about 250 µs (Couchman, Grothe, & Felmy, 2010; Figure 4A). At least synaptically, these short decay time constants would allow to detect the coincidence of excitatory currents for inputs up to about 4 kHz at least. Synaptic inhibition on the other hand exhibits much slower kinetics, decaying within about 1.5 ms (Couchman, Grothe, & Felmy, 2010; Myoga et al., 2014). Based on these studies, combined with other in vitro and in vivo data (Grothe & Sanes, 1994; Myoga et al., 2014; Pecka et al., 2008), the cycle-by-cycle action of inhibition is assumed to be restricted to frequencies up to approximately 1000 Hz (Figure 4B, C).

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Figure 4. Synaptic and membrane properties.

(A) Intracellularly recorded EPSCs evoked by local stimulation. Arrows mark jumps in amplitude evoked by increasing stimulation strengths.

(B) Same as A for IPSCs. A & B taken from Couchman et al. (2010), with permission.

(C) Intracellular membrane potential recordings for simulated trains (conductance clamp) of excitatory and inhibitory synaptic conductances (black) of 500 Hz (top) and 800 Hz (bottom). For each stimulus frequency, the pair of traces corresponds to inhibition lagging excitation by 0.1 ms (upper traces) and inhibition leading excitation by 0.6 ms (lower traces). Blue and red traces, respectively, depict results for simulated excitatory and inhibitory conductances only. Black dots mark peaks of the blue traces that are delayed with respect to the peaks of the black traces depending on the relative inhibitory lag/lead. From Myoga et al., (2014) with permission.

(D) Intracellularly recorded compound postsynaptic potentials evoked by distal auditory nerve stimulations in vitro. Note that EPSP components lag behind IPSP components. From Roberts et al. (2013), with permission.

(E) Input resistance and membrane time constant decrease by an order of magnitude during early postnatal development. From Scott et al. (2005), with permission.

(F) Left: Intracellular recording of action potentials evoked by half-sine current stimulations. APs (red) have low amplitudes. Right: The AP threshold is frequency dependent. Ordinate depicts the frequencies used to generate half sine wave stimuli (inverse period length). The threshold amplitudes (abscissa) are normalized to the minimum of each cell. From Lehnert et al. (2014), with permission.

(G) Computationally simulated membrane voltages for fast (active) Kv1 conductances (top) and slow, effectively passive leak conductances (bottom) for 100 Hz stimulations with inhibitory conductances. The conductance amplitude was adjusted to yield the same input resistance for both model variants (fast and slow conductances). Particularly for strong HCN currents (green), Kv1 decreases half-widths of IPSPs substantially. The purple trace was obtained for weak HCN currents. From Baumann et al. (2013), with permission.

(H) On-cell recording demonstrating stronger after-hyperpolarization after an action potential than after an EPSP. From Couchman et al. (2010), with permission.

The mechanism of integration of the four synaptic input pathways has been controversially discussed in the literature, particularly with regard to the ability of the glycinergic currents to contribute to cycle-by-cycle coincidence detection (Franken et al., 2015; Myoga et al., 2014; Pecka et al., 2008; Roberts, Seeman, & Golding, 2013). There is general agreement that excitatory coincidence detection constitutes the backbone of ITD processing (Franken et al., 2015; Jeffress, 1948; Joris, Smith, & Yin, 1998; Leibold, 2010; Myoga et al., 2014; Pecka et al., 2008) and also that the spike rate of MSO cells can largely be accounted for by a linear combination of the binaural inputs (Joris et al., 2006; van der Heijden et al., 2013; Plauška, Borst, & van der Heijden, 2016); but see (Franken et al., 2015). However, both the anatomical evidence for strong and relatively fast-acting (Figure 4B) and temporally preceding (Figure 4D) glycinergic inhibitory synapses (see section 1) and the effects of pharmacological blockade of glycinergic signaling on ITD tuning (Brand et al., 2002; Pecka et al., 2008) suggest a fundamental role of the two inhibitory pathways in shaping the ITD tuning curves, particularly in determining the position of the preferred ITD (Figure 4C). Explaining such inhibition-induced peak shifts in models in which inhibitory and excitatory currents act on the same time scale is quite straightforward (Brand et al., 2002; Leibold and & Hemmen, 2005), if the contralateral inhibition precedes excitation. Incorporation of the measured, slower inhibitory synaptic kinetics generally does not produce shifts in the preferred ITD (Day & Semple, 2011; Leibold & van Hemmen, 2005; Roberts, Seeman, & Golding, 2013; Zhou, Carney, & Colburn, 2005) for all delays between contralateral excitation and inhibition, but nevertheless shows such effects for a wide range of delays (Leibold, 2010; Myoga et al., 2014). The question of the functional role of inhibition for ITD processing will be discussed in detail later.

Subthreshold Channels

The main determinant of the short memory time scale of MSO neurons is their short membrane time constant of 300 µs and sometimes even less (Couchman, Grothe, & Felmy, 2010), which is attained during early development (Figure 4E). For comparison, typical membrane time constants of cortical neurons are in the range of 10 milliseconds (Stuart & Sakmann, 1995). Apart from the anatomical leakage contribution imposed by the thick proximal dendrites as described earlier, the time constant is mainly determined by the fact that transmembrane channels continue to conduct at rest. Two such channels have been described in great detail in the literature: (1) a hyperpolarization-activated, cyclic nucleotide-gated (HCN) channel (Baumann et al., 2013; Khurana et al., 2011), which generates a cationic inward current, and (2) a dendrotoxin (DTX)-sensitive voltage-gated potassium (Kv1) channel (Mathews et al., 2010; Svirskis et al., 2002), which generates an outward current. The two channels balance each other and thereby fix both the resting potential at about –67 mV (Couchman, Grothe, & Felmy, 2010) and the input resistance of 2–8 mohm in mature neurons (Couchman, Grothe, & Felmy, 2010; Scott, Mathews, & Golding, 2005; ). Apart from their directionality, the channels mainly differ in their time constants. Kv1 channels are fast opening (τ ~ 1.5 ms), whereas the HCN channel kinetics is much slower, with a reaction time of ~100 ms, far longer than the dynamics typically induced by the fine structure of the stimulus.

Functionally, the fast Kv1 kinetics has been shown to shorten EPSPs (Mathews et al., 2010; Scott, Mathews, & Golding, 2005; Svirskis et al., 2002; Svirskis, Dodla, & Rinzel, 2003), thereby further reducing the time window for coincidence detection of the AMPA-mediated synaptic potentials. The same argument, however, also applies to the additional sharpening of glycine-mediated inhibitory potentials (Baumann et al., 2013). Furthermore, sharpening can be achieved not only by fast Kv1 kinetics, but also by the HCN reversal potential of about –35 mV, which speeds up the decay of IPSPs (Baumann et al., 2013). Thus, the membrane potential dynamics results from an intricate non-linear interplay between all synaptic and channel kinetics.

In addition to temporal sharpening, fast Kv1 activation has been shown to underlie membrane potential resonances in MSO cells (Remme et al., 2014; Mikiel-Hunter, Kotak, & Rinzel, 2016; Fischer, Leibold, & Felmy, 2018), which would amplify input fine structure in the range of several hundred Hz. The functional role of these resonances is not fully understood. Modeling and in vitro physiology suggest that these resonances permit a frequency-dependent reduction of the spike threshold (Lehnert et al., 2014; Remme et al., 2014; Figure 4F).

The Kv1 kinetics has also been shown to affect preferred ITDs in vitro (Myoga et al., 2014) and in modeling studies (Jercog et al., 2010; Myoga et al., 2014). Since Kv1 opens with a short time constant of only ~1.5 ms, it will be sensitive to the slope of the subthreshold PSCs. Fast-rising PSCs will not be affected by Kv1 opening, whereas slow depolarization will be counterbalanced by Kv1 opening. This effective high-pass filtering will differentially affect the inputs from the two ears (and hence shift preferred ITD) if they have different slopes, either because of differential temporal jitter or differences in the relative delay between excitation and inhibition (Jercog et al., 2010; Myoga et al., 2014).

Based on computational modeling, Kv1 and HCN1 channels have been proposed to be non-isotropically distributed along the dendrites, exhibiting a higher density at the soma (Mathews et al., 2010). However, antibody staining of HCN also shows strong immunofluorescence at the dendrites(Khurana et al., 2011). The putative somatic colocalization of Kv1 channels and glycinergic synapses argues in favor of a functional role for both currents (Figure 4G) rather than cooperation between Kv1 channels and (spatially segregated) glutamatergic synapses (Baumann et al., 2013; Myoga et al., 2014). Moreover, since Myoga et al. (2014) have shown that temporal broadening of the EPSPs by adding jitter actually amplifies inhibition-induced peak shifts, the idea that Kv1 mainly sharpens the already ultrabrief EPSCs would not seem necessary, at least from the perspective of the inhibition model.

While bath application of DTX suppresses a large fraction of the potassium currents, a background of DTX-insensitive outward currents with slower kinetics still remains (Scott, Mathews and Golding, 2005), which argues for the presence of additional yet unidentified potassium channels with as yet unknown functional roles.

Action Potential Generation

MSO cells exhibit unusually small action potential (AP) amplitudes of 10 mV or less (Yin & Chan, 1990; Scott, Hage, & Golding, 2007; Couchman, Grothe, & Felmy, 2010; Figure 4F, H). For practical reasons, it has even been argued that in vitro APs can be more reliably detected by measuring the associated after-hyperpolarization (Figure 4H) rather than their upstroke and peak (Couchman, Grothe, & Felmy, 2010). The large somatic leak currents described in the previous section are the most obvious reason for tiny APs. But if most of the synaptic currents leave the cell through subthreshold channels, how can a sufficiently large proportion of the ions remaining actually reach the spike-generating zone of the thin axon and sustain excitability? In a modeling study based on measured axonal morphology, Lehnert et al. (2014) showed that the large somatic leak may indeed prevent the initiation of APs in the axon initial segment in response to fast fluctuating inputs, and shift the spike initiation zone to the proximal nodes of Ranvier. This result is consistent with the finding that sodium channels in the axons of high-frequency neurons of the avian nucleus laminaris are located further distally than those in low-frequency neurons (Kuba, Ishii, & Ohmori, 2006). Thus, the initial portion of the axon isolates the somatic leak from the spike-generating zone and the efficacy of the isolation needs to be larger the higher the input frequency, that is, the more difficult it is for the stimulus to elicit an AP.

In addition to the many circuit, synapse and membrane elements and mechanisms that have been proposed to determine the optimal ITD of a neuron (reviewed earlier), asymmetries in axonal morphology have also been considered. If the axon originates from the lateral dendrite, somatic inhibition would effectively delay the contralateral input, leading to contralaterally leading best ITDs (Zhou, Carney, & Colburn, 2005). Anatomical evidence for systematic axonal asymmetry, however, is absent (Rautenberg, Grothe, & Felmy, 2009).

Modulatory Influences

Since changes in ITD of only 10 µs have marked effects on MSO response rates, biophysical processes occurring on scales longer than about 5 ms cannot directly interact with the mechanism for coincidence detection of features in the fine structure of the stimulus. Nevertheless, such slow pathways have been described for MSO cells and play a role in controlling the excitability of the neuron.

HCN channels found in the axon initial segment decrease MSO spiking probability by adding a leak current (Ko et al., 2016). Serotonin released during states of increased attention shifts the membrane potential required for half-maximum activation of HCN to more negative voltages, thereby reducing leak and increasing spike probability (Ko et al., 2016).

Presynaptic GABAB receptors reduce the transmission of both the glutamatergic and the glycinergic MSO inputs (Stange et al., 2013). The GABA is supplied by a feedback loop which includes GABAergic neurons in the superior periolivary nucleus that are driven by the MSO, project back to it, and release GABA from extrasynaptic varicosities. Implications of this negative feedback on ITD encoding will be discussed next.

The Computation of Interaural Time Differences

As we have seen, principal MSO neurons fundamentally serve as coincidence detectors for their binaural inputs and each responds maximally to a specific range of ITDs. Although some MSO neurons are responsive (i.e., fire action potentials) to monaural stimulation of either ear alone, the sum of the two monaurally evoked responses is generally far below the response to binaural stimulation at favorable ITDs. Moreover, the preferred ITD can be predicted from the phase delay between the two monaural responses (Yin & Chan, 1990). Thus, to achieve maximal coincidence between ipsi- and contralateral inputs, the external delay (i.e., ITD) must be compensated for by the internal delay within the brain. Conversely, the effective internal delay determines the preferred ITD and thus the range of sensitivity (i.e., ITD tuning) of a given MSO neuron (and for a given stimulation frequency to be further discussed under “The Nature of Internal Delays”).

Generally, MSO neurons are very broadly tuned; that is, response rates are modulated over a large range of ITDs that exceeds the range of ITDs that are generated by the shape of the skull under anechoic conditions, the so-called “physiological range” (Figure 5). For the majority of recorded ITD functions—irrespective of species—the highest neuronal spatial sensitivity (the slope of the ITD rate function that conveys most information about changes in location) is positioned around the midline (Harper & McAlpine, 2004), and preferred ITDs mostly correspond to contralateral positions at the edge or even outside of the physiological range. More specifically, the preferred ITD of a cell depends on the best frequency (BF), irrespective of the head size of the species studied, and on average preferred ITDs increase with decreasing BF (Figure 5) (Middlebrooks et al., 1994; McAlpine, Jiang, & Palmer, 2001; Hancock & Delgutte, 2004; Pecka et al., 2008; Werner-Reiss & Groh, 2008). These data thus refuted the long-standing idea of the MSO as a distributed labeled-line encoder of azimuthal space (as had been found in owls and chicks) in which preferred ITDs in each frequency band are distributed within the physiological range (or even clustered around the midline).

What then is the nature of the neuronal ITD code—and hence of the internal delay—that underlies the BF dependency of ITD tuning in MSO neurons? In vivo recordings from the MSO are technically very challenging, and these questions are thus still subject of ongoing debate and investigation. A number of recent studies have included enormously difficult experiments that yielded exciting, but unfortunately contradictory, findings regarding the mechanism(s) used to create internal delays. We will first briefly review the current hypotheses in light of the known anatomy and physiology of the MSO circuit, and then turn to recent advances in our understanding of spatial coding in the MSO and mammals in general.

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Figure 5. Coding of ITDs in the MSO.

(A) Extracellular recording of a gerbil MSO neuron (from Pecka et al., 2008, figure 1). ITD response functions were obtained at five different pure tone frequencies (color-coded, BF = 250 Hz). The gray area demarcates the physiological range of ITDs, that is, range of ITDs generated by the inter-ear distance. The inset shows the corresponding frequency-phase plot for this neuron, from which CD (“characteristic delay,” the slope of the function) and CP (“characteristic phase,” the phase intercept of the function at 0 Hz) can be obtained.

(B) Best ITDs (peak of the ITD response function at BF) of gerbil MSO neurons lie predominately outside the physiological range and are negatively correlated with BF. The data shown is for 96 MSO neurons (pooled from Pecka et al., 2008; Stange et al., 2013; Dietz et al., 2014) and reveals a highly significant dependency of best ITD upon BF, with 75% of best ITDs lying outside the physiological range. Single-neuron data are shown in black, while mean values (±SD) within 200 Hz wide bins are given in brown.

(C) CP and CD of MSO neurons are significantly negatively correlated. Data are from the same neurons as in B. This correlation can potentially be exploited for the coding of sound source locations at the population level.

(D) Since the slopes of ITD functions from all neurons within an MSO typically exceed the physiological range of ITDs (a consequence of the dependency of best ITDs on BF, see panel B), left and right MSOs can be regarded as two oppositely tuned hemispheric channels for the coding of auditory space, in which the laterality of a sound source is defined by the relative activity levels of the two channels.

(E) A negative feedback loop between the MSO and the SPN causes a slow-acting, long-lasting reduction in the response of MSO neurons, which is proportional to their prior activity level (Stange et al., 2013). This response modulation is mediated by volume transmission of GABA onto the MSO, where it acts on GABAB receptors (mostly pre-synaptically).

(F) This activity-dependent adaptation introduces an asymmetry into hemispheric population coding, as strongly lateralized sound sources will induce adaptation of the contralateral channel only. This hemispheric asymmetry can be observed both at the level of MSO neurons and in the perception of human listeners (e.g., shifts in the perceived location of midline, red arrows, and dashed vertical line).

The Nature of Internal Delays

Important insights into possible mechanisms for the generation of internal delays come from the tuning of MSO neurons for sound frequency: by definition, the highest spike rates (and the greatest modulation of the response) are evoked by pure tones presented at a neuron’s BF. Shifting the pure-tone frequency away from the BF in either direction results in a systematic reduction in the maximum spike rates elicited, and in a reduction in the dynamic range of the spike rate elicited by different ITDs (Figure 5). More importantly, by testing ITD sensitivity in MSO neurons at multiple frequencies (or using broadband noise) one can determine their characteristic delay (CD), that is, the ITD for which the relative discharge rate is identical for different stimulus frequencies. Theoretically, for neurons with a pure axonal conduction delay from one or both ears (i.e., a pure time delay as generated by delay lines), the CD corresponds to the ITD at which response peaks are aligned for all stimulus frequencies to which the neuron is sensitive. As mentioned before, the observed dependency of best ITDs on BF (i.e., internal delays) in the MSO rules out a Jeffress-like topographic arrangement of best ITDs. Nonetheless, theoretically this BF dependency could still be achieved by systematic pattern of variation of axonal length from the two ears, analogous to the delay-line system found in birds (compare Figure 6). However, while the inhibitory pathway within the MSO circuit indeed exhibits anatomical adaptations to achieve superior conduction speed and timing relative to excitation (see first section and Figure 6), current anatomical data do not support the existence of any systematic arrangement of axon length within the excitatory inputs themselves (Karino et al., 2011). Moreover, CDs of MSO neurons are typically not found at response peaks, but tend to align along the slopes of the ITD functions. This suggests that ITD sensitivity in these neurons is not generated by a simple time delay mechanism and that some form of phase delay must be present (“CP,” the characteristic phase).

Given the anatomical and physiological evidence for inhibitory inputs to MSO neurons, it may not be surprising that CPs are indeed observed. In fact, it is well established that the constellation of ipsilateral excitation and contralateral inhibition from the MNTB for coincidence detection in the lateral superior olive (LSO) results in a CP of half a cycle. It is further well documented that the temporal sensitivity for the input timing between inhibition and excitation in the LSO lies in the range of tens to hundreds of microseconds (Tollin, 2003; Tollin & Yin, 2005; Beiderbeck et al., 2018). Inhibition is therefore likely to contribute to tuning internal delays to the relevant ITDs (Brand et al., 2002; Seidl & Grothe, 2005), reviewed in (Grothe, Pecka, & McAlpine, 2010). This, however, does not mean that other factors like axonal length are not important. It only means that there is no indication for systematic variations in other parameters and that such factors on their own cannot explain overall delays (see Figure 6).

Nevertheless, phase delays could also be generated by other mechanisms. Two recent studies were able to obtain in vivo whole-cell recordings from MSO cells, thus enabling direct examination of the changes in membrane polarization in MSO neurons during ITD processing.

Based on data from whole-cell and attached-cell recordings, van der Heijden and colleagues (van der Heijden et al., 2013) concluded that inhibition is not detectable during—and hence does not contribute to—ITD tuning of MSO cells. This apparent lack of discernible inhibition may be due to the fixed temporal relationship between contralateral excitation and inhibition, resulting in a net PSP which, at least without further manipulations, may not always allow one to disentangle the different synaptic components. Physiological studies of the MNTB-derived inhibitory input, however, have consistently found that contralateral inhibition precedes contralateral excitation (Roberts, Seeman, & Golding, 2013; Goldwyn et al., 2017; see Figure 4) and thereby changes the timing of the composite contralateral input (Myoga et al., 2014). Anatomical findings of thicker axons and unusual myelination geared for fast conduction times specifically within the inhibitory pathway to the MSO support these physiological results (Ford et al., 2015; Stange-Marten et al., 2017) that were already predicted based on theoretical considerations (Brand et al. 2002). Another in vivo whole-cell study of the MSO carried out additional pharmacological interventions during recordings, a feat requiring utmost technical ability (Franken et al., 2015). Unlike van der Heijden and colleagues (2013), these authors indeed observed prominent inhibition in their MSO recordings, albeit not consistently in all cells (which could be related to the relatively high CFs of the recorded neurons). Interestingly, they also consistently observed phasic hyperpolarizations prior to supra-threshold excitatory events. The authors concluded that this hyperpolarization might be caused by the action of additional, excitatory inputs that inactivate low-threshold potassium channels (Franken et al., 2015). However, the origin of this additional input is unclear.

Another, perhaps complementary, hypothesis for the generation of internal delays is based on the possible presence of small differences in spectral tuning between inputs from the two ears, and has been investigated in two other recent studies. The so-called stereausis hypothesis arose from the application of theories developed to account for binaural pitch detection based on coincidence detection (Loeb, White, & Merzenich, 1983) and later adapted for sound localization (Shamma, Shen, & Gopalaswamy, 1989). Analyses of monaural spike trains from auditory nerve recordings confirm the feasibility of this scenario (Joris et al., 2006), and one study indicates that such a mechanism may contribute to ITD tuning under some conditions involving complex stimuli (Benichoux et al., 2015). In contrast, studies using simple dichotic stimuli indicate no or, at best, only a minor and non-systematic contribution of stereausis to ITD tuning (Pecka et al., 2008; Plauška, van der Heijden, & Borst, 2017).

In summary, the nature of the internal delay in MSO neurons is still not clear. Quite possibly, many, if not all of the mechanisms mentioned previously (including conduction delay) may contribute. Nonetheless, one factor often neglected in considerations of the role of inhibitory inputs is the intriguing possibility that the characteristic delay is adjusted and/or refined during ontogeny. Anatomical refinement of glycinergic inhibition has been shown to be at least partially dependent on auditory experience during early development, and auditory deprivation can alter the distribution of best ITDs. Thus, while other mechanisms (axonal length, cochlear delays etc.) can establish an approximate BITD early in development, an experience-dependent process could selectively optimize inhibitory inputs following the onset of hearing to achieve and maintain the desired tuning.

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Figure 6. Complementary mechanisms that (potentially) contribute to the internal delay in circuits for ITD computation.

Mechanisms suggested or established for the mammalian MSO (green boxes) and avian NL (brown boxes).

(A) Cochlear delays: The traveling wave of the cochlear basilar membrane excites basal regions earlier than apical regions. This mechanical delay of excitation could potentially be exploited by a systematic mismatch in CF of inputs from the two ears to the MSO (Schroeder, 1977; Shamma et al., 1989). However, while discrepancies in frequency tuning between ipsi- and contralateral MSO inputs have been reported, no systematic mismatch has been found (Plauška et al., 2017). An as yet unexplored variation of this hypothesis posits a systematic mismatch between excitatory and inhibitory inputs from the same ear, which—in addition to differences in axon morphology (see panel B)—could contribute to the observed temporal precedence of inhibition (see Figure 4)

(B) Action potential propagation: Both in mammalian and avian ITD detector circuits, axon morphology contributes to the tuning of input times, albeit in distinct ways. In the MSO circuit (green box), axon diameter and myelination vary between inputs. The graph shows computer simulations of the dependence of conduction velocity upon the ratio of internode length to axon diameter in low-frequency GBCs (solid red line), high-frequency GBCs (dashed red line), and SBC axons (orange line). Arrows denote the ratios found in the gerbil. SBCs that provide the contralateral excitatory input to the MSO have thinner axons and larger internode lengths than the axons of GBCs that innervate the contralateral MNTB (and thus are part of the inhibitory MSO input). Notably, the ratios for low-frequency GBCs (but not high-frequency GBCs) deviate significantly from the canonical value of 100 because of the unusually short intermodal length given the axonal diameter. This extraordinary patterning of myelination is restricted to low-frequency GBCs and is associated with a further increase in conduction speed, suggesting a specific morphological adaptation of the contralateral inhibitory pathway for fast AP conduction for ITD processing (see also Stange-Marten et al., 2017). Graph re-drawn and modified from Ford et al. (2015). Additionally, axons of SBCs exhibit differential myelination and variation in axon caliber depending on whether they terminate on the ipsilateral or the contralateral MSO (Seidl & Rubel, 2016).

In the avian nucleus laminaris (NL, brown box), internal delays are generated via axonal delay lines, that is, systematic variation of axonal length and caliber (and myelination) to equalize conduction times between ipsi- and contralateral inputs (Jeffress, 1948; Seidl et al., 2014).

(C) Post-synaptic integration: Upper box: Internal delays in the MSO could be created by the observed temporal precedence of contralateral inhibition relative to contralateral excitation, which may result in a delayed net contralateral post-synaptic potential (Pecka et al., 2008; Myoga et al., 2014). Lower box: Alternatively or additionally, asymmetries in the distribution of activation dynamics of somatic ion channels (such as low-threshold potassium channels) could induce asymmetries in temporal filtering that would result in an effective internal delay (Jercog et al., 2010; Franken et al., 2015).

The Nature of the Spatial Code and Dynamics of Spatial Processing

The fact that ITD functions of MSO neurons show very broad tuning (essentially linear modulation between -90° and 90°) that is stereotypical within a given spectral band stimulated the idea of hemispheric, oppositely coding channels on each side of the brain that might be compared at later stages of the pathway (Figure 5) (McAlpine, Jiang, & Palmer, 2001; Stecker & Middlebrooks, 2003; Hancock & Delgutte, 2004; Harper & McAlpine, 2004). This concept relies on the idea that similar activity levels in both channels should encode sound-source position at the midline, such that a relative increase in activity in one of the brain hemispheres would indicate a corresponding contralateral location with respect to the more active brain hemisphere. This strategy might well optimize coding efficiency in animals with small head sizes, but might be sub-optimal for animals with larger heads and high frequency hearing, owing to the increasing ambiguity of spike-rate-to-ITD mapping (Harper & McAlpine, 2004; Lüling et al., 2011; Goodman, Benichoux, & Brette, 2013; Harper et al., 2014). Such ambiguity could, however, be eliminated by making use of additional information from other ITD-sensitive channels (such as the low-frequency neurons in the Lateral Superior Olive, see next paragraph) during the decoding (Lingner et al., 2018). One interesting observation in this regard is that CD and BF, as well as CD and CP, are negatively correlated (Figure 5); (Agapiou & McAlpine, 2008; Lüling et al., 2011). This correlation could be neuronally exploited to acquire frequency-invariant information about ITDs, either in the context of population average coding (Lüling et al., 2011) or a pattern recognition approach (Goodman, Benichoux, & Brette, 2013; Benichoux et al., 2015).

While the models for possible ITD representation are manifold (see preceding discussion and Day & Delgutte, 2013), elegant adaptation paradigms with human subjects not only strongly imply that a population code underlies sound localization in humans/primates, but have also highlighted the fact that prior stimulation influences subsequent spatial perception (Phillips & Hall, 2005; Vigneault-MacLean, Hall, & Phillips, 2007). Traditionally, such adaptive coding strategies have not been associated with sound localization, yet recent findings from both human psychophysics (Getzmann, 2004; Phillips & Hall, 2005; Vigneault-MacLean, Hall, & Phillips, 2007; Maier et al., 2009; Dahmen et al., 2010; Lingner et al., 2018) and animal physiology (Magnusson et al., 2008; Park et al., 2008; Dahmen et al., 2010; Stange et al., 2013) have clearly demonstrated a dependency of spatial tuning, and even spatial perception, on the preceding stimulus (specifically, on the properties of the stimulus and their temporal profile). It has become apparent that dynamic adaptation mechanisms act on spatial processing in the brainstem, including the MSO and LSO (Magnusson et al., 2008; Stange et al., 2013). The response levels of individual neurons are negatively correlated with their prior spiking activity, which is typical of gain modulation. Notably, this modulation, which acts on time scales of tens of milliseconds and can last for seconds, is mediated by GABAB receptor signaling, even though MSO cells are not themselves GABAergic. Instead, as already described, a di-synaptic feedback-loop exists, with MSO neurons innervating GABAergic neurons in the nearby Superior Paraolivary Nucleus, which subsequently feed back onto the MSO (Figure 5; Stange et al., 2013).

The resulting activity-dependent rate adaptations do not shift the preferred ITDs of MSO neurons directly; that is, best ITDs are not altered. However, these modulations have significant consequences at the level of hemispheric population coding, because of the activity-dependent nature of the adaptation. For example, a strongly lateralized sound source will generate unequal adaptation in the two hemispheres, with pronounced rate adaptation only in the contralateral channel. Accordingly, this hemispheric asymmetry should shift the perceived location of a subsequently presented sound source (Figure 5). As noted earlier, Phillips and colleagues first tested this hypothesis in a number of psychophysical paradigms, and were able to demonstrate a pronounced shift in the perceived location of sound sources after prior presentation of a lateralized adapter in human listeners (Phillips & Hall, 2005; Vigneault-MacLean, Hall, & Phillips, 2007). Stange et al. (2013) have demonstrated that GABAB-mediated rate adaptation in the MSO is sufficient to explain these shifts in human perception. Moreover, we have recently developed a new decoding model in which sound location is initially computed in both brain hemispheres independently between MSO and LSO and then combined to yield a hemispherically balanced code. This model not only closely captures the observed absolute localization errors caused by stimulus history, but also predicts a selective dilation and compression of perceptional space (Lingner et al., 2018). These model predictions are confirmed by improvement and degradation of spatial resolution in human listeners. shown that the primary function of these perceptual shifts seems to be the relative segregation of the adapting and the subsequent sound source. Specifically, as the reported shifts in location are directed away from the adapter location, the perceived distance between the sound sources is increased relative to the actual distance (Lingner et al., 2018).

Taken together, these findings suggest that ITD coding in the MSO serves to encode the relative separation of concurrent or subsequent sound sources, rather than to provide an absolute representation of position in space.

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