Directional Hearing in Fishes 507 3. The TS has a columnar organization with similar best axes of horizontal motion tending to be constant within vertical columns (Wubbles et al. 1995, Wubbles and Schellart 1998). 4. Some phase-locked units had phase angles of synchronization that did not vary with the stimulus axis angle (except for the expected 180 o shift at one angle), while others showed a phase shift that varied continuously with stimulus angle over 360 o (Wubbles and Schellart 1997). Wubbles and Schellart concluded that those and other results strongly supported the phase model. They speculated that the rostro-caudally oriented units of the medial TS were channels activated by swim bladder-dependent motion input, while the diversely oriented units of the lateral TS represented direct motion input to the otolith organs. The utricle was thought to be the otolith organ supplying the direct motion-dependent input because of its horizontal orientation. The authors speculated that the units with synchronization angles independent of stimulus direction represented pressure-dependent swim bladder inputs while the units with variable synchronization phase angles represented direct motion inputs. Wubbles and Schellart (1997) then concluded that “…the phase difference between the(se) two unequivocally encodes the stimulus direction (0-360 o )…” (i.e., solves the 180 o ambiguity problem). This conclusion would be strengthened by a more clear and detailed explanation for the direction-dependent variation in synchronization angle shown by some units and by a testable theory for the final step that solves the 180 o ambiguity. 8. Summary and conclusions 1. There are much data on the accoustical behaviors of several fish species that strongly suggest the capacity directional hearing and sound source localization. Most of these observations indicate the necessity that one or more otolith organs respond to acoustic particle motion. 2. The question of localization in the near- versus far-fields is no longer a critical issue because we now know that near field hearing does not imply that the lateral line system must be involved. The otolith organs respond directly to acoustic particle motion in both fields. 3. Most conditioning and psychophysical studies on the discrimination of sound source location provide evidence consistent with the hypothesis that fishes are able to locate sound sources in a way analogous to localization capacities of human beings and other tetrapods, both in azimuth and elevation. However, most of these studies fail to unequivocally demonstrate that fishes can actually perceive the location of sound sources. 4. An explanation for sound source localization behavior at the level of Mauthner cells and other reticulo-spinal neurons cannot serve to explain conditioning and discrimination learning phenomena with respect to source location. 5. All present accounts postulate that the process begins with the determination of the axis of acoustic particle motion by processing the profile of activity over an array of peripheral channels that directly reflect diverse hair cell and receptor organ orientations (“vector detection”). 6. Neurophysiological studies on cells of the auditory nerve and brainstem are consistent with vector detection and show that most brainstem cells preserve and enhance the Advances in Sound Localization 508 directionality originating from otolith organ hair cells. Goldfish and other Otophysi present a clear problem for this view because there is little or no variation of hair cell directionality in the saccule or at the midbrain. This has lead to speculations that Otophysi use other otolith organs (lagena or utricle) in addition to the saccule for vector detection. 7. Vector detection leaves an essential “180 o ambiguity” as an unsolved problem (Which end of the axis points to the source, or, in what direction is the sound propagating?). The “phase model” of directional hearing has been moderately successful in solving this ambiguity in theory and experiment. However, the 180 o ambiguity is not the only ambiguity for sound source localization throughout the vertebrates. It is not certain that auditory processing, alone, must be able to solve this problem. 8. Although the phase model is successful in a general sense, it is difficult to apply in several important cases (i.e., for fishes without swimbladders, and for Otophysi) where effectively independent representations of the particle motion and pressure waveforms are required but are not evident. 9. Additional problems for vector detection and the phase model are that the axis of acoustic particle motion points directly at the source only for monopole sources, and that clear and unambiguous representations of waveform phase that could help in localization have not been observed in auditory nerve units (distributions of phase- locking angles tend to be uniform). 10. While there are behavioral and electrophysiological observations that are consistent with sound source localization in fishes, there are no examples of localization capacities in a single species that have a comprehensive theoretical explanation. Sound source localization in fishes remains incompletely understood. 9. References Allen, J. (1996). OHCs shift the excitation pattern via BM tension, In: Diversity in Auditory Mechanics, Lewis, E.R.; Long, G.R.; Lyon, R.F.; Narins, P.M.; Steele, C.R. & Hecht- Poinar, E., (Eds.), pp. 167-175, World Scientific Publishers, Singapore Berg, A.V. van den & Schuijf, A. (1983). 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Sound source localization of a dipole by the plainfin midshipman fish (Porichthys notatus). Journal of the Acoustical Society of America, 127, pp. 1886 (Abstract) 27 Frequency Dependent Specialization for Processing Binaural Auditory Cues in Avian Sound Localization Circuits Rei Yamada and Harunori Ohmori Kyoto University Japan 1. Introduction Localizing sound sources is essential for survival of animals. It enables animals to avoid danger, or to catch their prey. The differences of sound information between two ears, those of interaural time and level difference (ITD and ILD), are important cues for sound source localization. The minimum resolvable angle of sound source separation is less than 30˚ along the horizontal plane in many species (cat, Casseday & Neff, 1973; rat, Masterton et al., 1975; songbirds, Klump et al., 1986; Park & Dooling, 1991; Klump, 2000), and in some species the resolution is extremely high. In human and in barn owl, the resolvable angle is as small as 1˚ (Mills, 1958; Knudsen & Konishi, 1979). ITD and ILD cues depend on the head size of animals and are quite small, particularly in small-headed animals. Thus processing of these cues may need specialization of individual neurons and neural circuits. The time and level information of sounds are captured in the cochlea, transformed to trains of action potentials in the auditory nerve fibers, and then transmitted to auditory nuclei in the brainstem. In the brainstem, time and level information are extracted in the cochlear nucleus and then transmitted in parallel pathways which are specialized to process ITD and ILD cues separately (Fig. 1A, indicating the auditory brainstem circuit in birds) (Sullivan & Konishi, 1984; Takahashi et al., 1984; Takahashi & Konishi, 1988; Warchol & Dallos, 1990; Moiseff & Konishi, 1983; Yin, 2002). Furthermore, in the auditory system, neurons are tuned to a specific frequency of sound (characteristic frequency, CF), and ITD and ILD cues are processed by each CF neuron (Brugge, 1992; Klump, 2000). Recently, a series of studies in the chicken have revealed several frequency dependent specializations in ITD coding pathway (Kuba et al., 2005; Yamada et al., 2005; Kuba et al., 2006). These specializations include the type and the density of ion channels, and their subcellular localization. Furthermore, recent observations in mammals and birds indicate that time and level information are not processed independently but rather cooperatively to enhance the contrast of interaural difference cues even at the first stage of processing of these cues in the brainstem auditory nuclei (Brand et al., 2002; Nishino et al., 2008; Sato et al., 2010). In this chapter, we will first summarize what is known about the neural specializations that enable the preciseness of coincidence detection of synaptic inputs, which is central to process the ITD. And then, we will review observations on how the interaction of time and level information of sounds modulates the processing of each ITD and ILD cue. Advances in Sound Localization 514 A Coincidence detector from contra NM from ipsi NM Delay line B NL NA NL SON ANF NM Time Level ITD ILD LLD Midline Cochlear nucleus LLD SON NL NA NM Excitation Inhibition Midline Fig. 1. (A) Schematic diagrams of the auditory brainstem circuits for processing ITD and ILD in birds. (B) Modification of Jeffress model incorporating features of NL of the chick. The contralateral projections from NM to NL form delay lines, while NL neurons act as coincidence detectors of bilateral excitatory inputs. When the sound source moves toward more contralateral locations, spikes from contralateral NM will arrive at NL faster, and bilateral spikes arrive simultaneously at the NL neuron located more laterally. 2. Specialization of ITD coding neurons Extraction of ITDs in birds is explained on the classical Jeffress model (Jeffress, 1948), which requires delay lines and an array of coincidence detectors (Fig. 1B). Delay lines delay the arrival time of action potential to the coincidence detectors, while the coincidence detectors fire maximally when they receive synaptic inputs simultaneously from both ears. These two elements allow each ITD to be encoded as the place of neuron in the neuronal array. In birds, ITDs are processed in the nucleus laminaris (NL, Fig. 1A) (Konishi, 2003), which is a homologue of the mammalian nucleus of the medial superior olive (MSO). NL is innervated bilaterally from the nucleus magnocellularis (NM). NM extracts fine temporal information of sounds from auditory nerve fibers. In the chicken, the projection fibers from contralateral NM to NL form delay lines (Young & Rubel, 1983; Carr & Konishi, 1988), while NL neurons act as coincidence detectors of bilateral synaptic inputs (Fig. 1B) (Carr & Konishi, 1990; Overholt et al., 1992). Sensitivity to ITDs is extremely high in NL neurons. In vivo single-unit studies in the barn owl NL showed that the half-peak width of the ITD tuning curve varies with the CF of neurons, and reaches about 0.1-0.2 ms at 3-7 kHz (Carr & Konishi, 1990; Fujita & Konishi, 1991). This sharpness of ITD tuning of NL neurons should underlie the resolution of a microsecond order of ITDs in the barn owl (Moiseff & Konishi, 1981) and should be determined by the coincidence detection of NL neurons. The cellular mechanism of coincidence detection in NL neurons was studied in vitro (Kuba et al., 2003). Experiments were made in brainstem slices of the posthatch chick of P3-P11 at the body temperature of birds (40˚C). Under the whole-cell recording, EPSPs were evoked in NL neurons by electrical stimuli applied to both sides of projection fibers from NM, while the time interval between the two stimuli (∆t) was varied (Fig. 2A). The EPSPs were summated to generate an action potential as the interval of two stimuli decreased. The probability of firings peaked at ∆t of 0 ms (Fig. 2A and B), and the half-peak width of the coincidence detection curve (time window) was 0.4 ms (Fig. 2B), which is comparable to that observed in the barn owl NL in vivo (Carr & Konishi, 1990). What cellular mechanisms underlie to achieve such a high accuracy of coincidence detection? Frequency Dependent Specialization for Processing Binaural Auditory Cues in Avian Sound Localization Circuits 515 The acceleration of EPSP time course is essential for the accurate coincidence detection (Kuba et al., 2003) by limiting the time window for the summation of bilateral EPSPs. NL neurons reduce their input resistance extensively by activating several membrane conductances at the resting membrane potential (Reyes et al., 1996; Trussell, 1999; Kuba et al., 2002; Kuba et al., 2003). Among them, the most important is the conductance of low- threshold K + current (I KLT ). I KLT is mediated by subtypes of voltage-gated K + channels, Kv1.1 and 1.2, and in particular, Kv1.2 channels are predominant in the NL (Fukui & Ohmori, 2004; Kuba et al., 2005). Developmentally, I KLT increases nearly fourfold around the hatch, and becomes the dominant conductance at resting potential in NL neurons (Kuba et al., Fig. 2. Rapid EPSP time course is essential for coincidence detection (from Kuba et al., 2003; Kuba et al., 2005). (A) Bilateral EPSPs are evoked at different time intervals (∆t). Spikes are generated when ∆t is small. (B) Probability of spike generation as a function of ∆t. The time window is indicated by the horizontal broken line. (C) EPSPs from the same NL neurons at different holding potentials. EPSP is accelerated with membrane depolarization (from -62 to -52 mV). Data are from middle CF neurons. (D) Time window of coincidence detection at each CF. (E) EPSPs from each CF are normalized and superimposed. EPSP is fastest and coincidence detection is the most accurate at middle CF. Advances in Sound Localization 516 2002). Moreover, it is activated near the resting membrane potential with rapid kinetics (-60 mV; Rathouz & Trussell, 1998). I KLT is activated by a small membrane depolarization and accelerates the falling phase of EPSP. Consequently, EPSP has a fast time course as fast as EPSC at the resting membrane potential, and is even faster than EPSC with a small membrane depolarization (Fig. 2C). These findings indicate that I KLT plays a crucial role in shortening the time window of coincidence detection to submillisecond order. Recently, a similar developmental increase of I KLT has been reported to shape the EPSPs in the mammalian MSO neurons (Scott et al., 2005). 3. Frequency specific expression of I KLT Although the range of audible frequencies varies among species, precision is the highest in the middle frequencies in most avian species; thus the acuity of azimuthal sound source localization depends on the sound frequency (Klump, 2000). NL is organized tonotopically; the CF of neurons is high in the rostro-medial (high CF) NL and decreases monotonically to the caudo-lateral (low CF) NL (Rubel & Parks, 1975). ITDs are determined separately by frequency-specific NL neurons. The coincidence detection is dependent on the frequency region of NL (Kuba et al., 2005), and their time window of coincidence detection was the smallest at the middle CF neurons, closely followed by the high CF neurons, and was the largest at the low CF neurons (Fig. 2D). Thus the acuity of coincidence detection is the highest in the middle CF NL neurons. The EPSP time course is the fastest in the middle CF NL neurons (Fig. 2E). The size of I KLT conductance is the largest at the middle CF. The expression of Kv1.2 channels is the highest in the middle CF neurons, followed by the high CF neurons, and is the lowest in the low CF neurons (Kuba et al., 2005). These observations indicate that the high level of Kv1.2 expression accelerates the EPSPs and determines the tonotopy of the coincidence detection in NL. Thus, the dominant expression of Kv1.2 may underlie the high resolution of sound localization in the middle frequency range in avian species (Klump, 2000). 4. HCN channel Hyperpolarization-activated cation current (I h ) is another major conductance activated at the resting membrane potential in NL neurons (Kuba et al., 2002). I h has slow activation and deactivation kinetics, and has the reversal potential positive to the resting membrane potential (-50 to -20 mV) (Pape, 1996). These allow I h to accelerate the EPSPs in two ways. First, it works as a shunting conductance to shorten the membrane time constant. Second, it depolarizes the resting membrane potential and activates I KLT . Thus, I h contributes to improve the coincidence detection. I h is mediated by HCN (hyperpolarization-activated and cyclic nucleotide-gated) channels and four channel subtypes have been described (HCN1 ~ 4) with different rates of activation and different sensitivities to cyclic nucleotides (Santoro & Tibbs, 1999). Expressions of HCN1 and HCN2 are demonstrated in NL neurons and the level of expression varies along the tonotopic axis (Yamada et al., 2005). HCN1 is expressed highest at the low CF and decreases toward the high CF NL region, while HCN2 is evenly distributed along the tonotopic axis. What is the functional significance of this CF-dependent expression of HCN channels? HCN1 channels have a more positive activation voltage than HCN2 channels (Santoro & Tibbs, 1999). Because of the predominant expression of HCN1 channels, I h [...]... of firing rate between the peak and trough of the ITD tuning curve) in the high CF neurons 6 Sound level dependent inhibition modulates the ITD tuning in NL Processing of ITDs in NL in vivo is affected by sound loudness Loud sound was expected to reduce the peak-trough contrast by simulation (Dasika et al., 2005) However, the peaktrough contrast was maintained rather at high sound pressure level in the... the NA and makes an inhibitory innervation to NA, NM, and NL in a sound- level-dependent manner (Lachica et al., 1994; Yang et al., 1999; Monsivais et al., 2000; Burger et al., 2005; Fukui et al, 2010) By recording single unit activity in NL of chicken in vivo, the ITD tuning in NL is found being controlled by both the frequency and level of sounds (Nishino et al., 2008) In the following discussion, best... in this unit The rate-ILD relationship varied with the IPD in both units, and the firing rate was lowest for the in- phase sound (0º IPD, thick solid lines), and the rate increased in most cases when IPD was included, to some extent In the open field, any displacement of the sound source from the midline must cause a correlated change in both the level and phase of sounds between two ears When the sound. .. also sensitive to the sound level (Tollin & Yin, 2005) In fact, the ITD tuning of MSO neurons could be maintained even at loud sound (Pecka et al., 2008) It has also been shown that the processing of ILD in LSO, which is a homologue of the LLD in birds, depends critically on timing; the timing of contralateral inhibition through MNTB has to be matched with ipsilateral excitation (Finlayson & Caspary,... Specialization for Processing Binaural Auditory Cues in Avian Sound Localization Circuits 523 10 References Adams, J.C & Mugnaini, E (1990) Immunocytochemical evidence for inhibitory and disinhibitory circuits in the superior olive Hear Res., 49, 281-298, Aston-Jones, G & Bloom, F.E (1981) Activity of NE-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle J Neurosci.,... source of inhibitory feedback for coincidence detection in the avian auditory brainstem J Neurosci., 19, 2313-2325, Yin, T.C (2002) Neural Mechanisms of Encoding Binaural Localization Cues in the Auditory Brainstem In: Integrative Functions in the Mammalian Auditory Pathway, Oertel, D, (Ed), 99-159, Springer-Verlag, New York Young, S.R & Rubel, E.W (1983) Frequency-specific projections of individual... that inhibition from the superior olivary nucleus (SON) controls Frequency Dependent Specialization for Processing Binaural Auditory Cues in Avian Sound Localization Circuits 519 ITD tuning in NL, rendering it tolerant to sound pressure level The level information of sound is extracted in the nucleus angularis (NA), which is another subdivision of cochlear nucleus (Fig 1A) The SON receives excitatory inputs... contralateral sound Activity in the NA is affected by strong contralateral sound through the interaural canal, an air-filled connection between the two middle ear cavities (Fig 5A) During the binaural sound stimulus, the interaction of contralateral sound shows IPD dependence (Fig 5B) Increasing the level of out-of-phase (IPD = 180º) contralateral sound monotonically increased the firing rate of the... of sound localization when animals are listening carefully to the sounds 5 Specialization of action potential initiation site along the tonotopic axis NL neurons are also specialized along the tonotopic axis in initiating action potentials in the axon The axon initial segment has a high density of Nav channels (Catterall, 1981), and is the site of action potential initiation in many neurons (Mainen... concluded to regulate the ITD tuning in NL The computer simulation that is based on a NEURON model reproduced a level dependence of ITD tuning in NL neurons (Nishino et al., 2008) The simulation further showed that without balance in the bilateral excitation, the peak-trough contrast of ITD tuning lost tolerance to the loud sounds The SON inhibition might also play a role in maintaining the balance of excitation . recording single unit activity in NL of chicken in vivo, the ITD tuning in NL is found being controlled by both the frequency and level of sounds (Nishino et al., 2008). In the following discussion,. of firing rate between the peak and trough of the ITD tuning curve) in the high CF neurons. 6. Sound level dependent inhibition modulates the ITD tuning in NL Processing of ITDs in NL in vivo. Binaural Auditory Cues in Avian Sound Localization Circuits 519 ITD tuning in NL, rendering it tolerant to sound pressure level. The level information of sound is extracted in the nucleus angularis