Barn owls are capable of great accuracy in detecting the interaural time differences (ITDs) that underlie azimuthal sound localization. They compute ITDs in a circuit in nucleus laminaris (NL) that is reorganized with respect to birds like the chicken. The events that lead to the reorganization of the barn owl NL take place during embryonic development, shortly after the cochlear and laminaris nuclei have differentiated morphologically. At first the developing owl’s auditory brainstem exhibits morphology reminiscent of that of the developing chicken. Later, the two systems diverge, and the owl’s brainstem auditory nuclei undergo a secondary morphogenetic phase during which NL dendrites retract, the laminar organization is lost, and synapses are redistributed. These events lead to the restructuring of the ITD coding circuit and the consequent reorganization of the hindbrain map of ITDs and azimuthal space.
avian development; morphogenesis; auditory; laminaris; evolution; interaural time difference
In the auditory system, precise encoding of temporal information is critical for sound localization, a task with direct behavioral relevance. Interaural timing differences are computed using axonal delay lines and cellular coincidence detectors in nucleus laminaris (NL). We present morphological and physiological data on the timing circuits in the emu, Dromaius novaehollandiae, and compare these results with those from the barn owl (Tyto alba) and the domestic chick (Gallus gallus). Emu NL was composed of a compact monolayer of bitufted neurons whose two thick primary dendrites were oriented dorsoventrally. They showed a gradient in dendritic length along the presumed tonotopic axis. The NL and nucleus magnocellularis (NM) neurons were strongly immunoreactive for parvalbumin, a calcium-binding protein. Antibodies against synaptic vesicle protein 2 and glutamic acid decarboxlyase revealed that excitatory synapses terminated heavily on the dendritic tufts, while inhibitory terminals were distributed more uniformly. Physiological recordings from brainstem slices demonstrated contralateral delay lines from NM to NL. During whole-cell patch-clamp recordings, NM and NL neurons fired single spikes and were doubly-rectifying. NL and NM neurons had input resistances of 30.0 ± 19.9 MΩ and 49.0 ± 25.6 MΩ, respectively, and membrane time constants of 12.8 ± 3.8 ms and 3.9 ± 0.2 ms. These results provide further support for the Jeffress model for sound localization in birds. The emu timing circuits showed the ancestral (plesiomorphic) pattern in their anatomy and physiology, while differences in dendritic structure compared to chick and owl may indicate specialization for encoding ITDs at low best frequencies.
avian; nucleus laminaris; nucleus magnocellularis; dendrite; coincidence detection; sound localization
The robust representation of the environment from unreliable sensory cues is vital for the efficient function of the brain. However, how the neural processing captures the most reliable cues is unknown. The interaural time difference (ITD) is the primary cue to localize sound in horizontal space. ITD is encoded in the firing rate of neurons that detect interaural phase difference (IPD). Due to the filtering effect of the head, IPD for a given location varies depending on the environmental context. We found that, in barn owls, at each location there is a frequency range where the head filtering yields the most reliable IPDs across contexts. Remarkably, the frequency tuning of space-specific neurons in the owl's midbrain varies with their preferred sound location, matching the range that carries the most reliable IPD. Thus, frequency tuning in the owl's space-specific neurons reflects a higher-order feature of the code that captures cue reliability.
The ability to locate where a sound is coming from is an essential survival skill for both prey and predator species. A major cue used by the brain to infer the sound's location is the difference in arrival time of the sound at the left and right ears; for example, a sound coming from the left side will reach the left ear before the right ear.
We are exposed to a variety of sounds of different intensities (loud or soft), and pitch (high or low) emitted from many different directions. The cacophony that surrounds us makes it a challenge to detect where individual sounds come from because other sounds from different directions corrupt the signals coming from the target. This background noise can profoundly affect the reliability of the sensory cue.
When sounds reach the ears, the head and external ears transform the sound in a direction-dependent manner so that some pitches are amplified more than other pitches for specific directions. However, the consequence of this filtering is that the directional information about a sound may be altered. For example, if two sounds of a similar pitch but from different locations are heard at the same time, they will add up at the ears and change the directional information. The group of neurons that respond to that range of pitches will be activated by both sounds so they cannot provide reliable information about the direction of the individual sounds. The degree to which the directional information is altered depends on the pitch that is being detected by the neurons; therefore detection of a different pitch within the sound may be a more reliable cue.
Cazettes et al. used the known filtering properties of the owl's head to predict the reliability of the timing cue for sounds coming from different directions in a noisy environment. This analysis showed that for each direction, there was a range of pitches that carried the most reliable cues. The study then focused on whether the neurons that represent hearing space in the owl's brain were sensitive to this range.
The experiments found a remarkable correlation between the pitch preferred by each neuron and the range that carried the most reliable cue for each direction. This finding challenges the common view of sensory neurons as simple processors by showing that they are also selective to high-order properties relating to the reliability of the cue.
Besides selecting the cues that are likely to be the most reliable, the brain must capture changes in the reliability of the sensory cues. In addition, this reliability must be incorporated into the information carried by neurons and used when deciding how best to act in uncertain situations. Future research will be required to unravel how the brain does this.
barn owl; neural coding; cue reliability; sound localization; other
Performing sound recognition is a task that requires an encoding of the time-varying spectral structure of the auditory stimulus. Similarly, computation of the interaural time difference (ITD) requires knowledge of the precise timing of the stimulus. Consistent with this, low-level nuclei of birds and mammals implicated in ITD processing encode the ongoing phase of a stimulus. However, the brain areas that follow the binaural convergence for the computation of ITD show a reduced capacity for phase locking. In addition, we have shown that in the barn owl there is a pooling of ITD-responsive neurons to improve the reliability of ITD coding. Here we demonstrate that despite two stages of convergence and an effective loss of phase information, the auditory system of the anesthetized barn owl displays a graceful transition to an envelope coding that preserves the spectrotemporal information throughout the ITD pathway to the neurons of the core of the central nucleus of the inferior colliculus.
Barn owls are nocturnal predators that rely on both vision and hearing for survival. The optic tectum of barn owls, a midbrain structure involved in selective attention, has been used as a model for studying visual-auditory integration at the neuronal level. However, behavioral data on visual-auditory integration in barn owls are lacking. The goal of this study was to examine if the integration of visual and auditory signals contributes to the process of guiding attention toward salient stimuli. We attached miniature wireless video cameras on barn owls’ heads (OwlCam) to track their target of gaze. We first provide evidence that the area centralis (a retinal area with a maximal density of photoreceptors) is used as a functional fovea in barn owls. Thus, by mapping the projection of the area centralis on the OwlCam’s video frame, it is possible to extract the target of gaze. For the experiment, owls were positioned on a high perch and four food items were scattered in a large arena on the floor. In addition, a hidden loudspeaker was positioned in the arena. The positions of the food items and speaker were changed every session. Video sequences from the OwlCam were saved for offline analysis while the owls spontaneously scanned the room and the food items with abrupt gaze shifts (head saccades). From time to time during the experiment, a brief sound was emitted from the speaker. The fixation points immediately following the sounds were extracted and the distances between the gaze position and the nearest items and loudspeaker were measured. The head saccades were rarely toward the location of the sound source but to salient visual features in the room, such as the door knob or the food items. However, among the food items, the one closest to the loudspeaker had the highest probability of attracting a gaze shift. This result supports the notion that auditory signals are integrated with visual information for the selection of the next visual search target.
saliency; saccades; multisensory; visual search; barn owls; selective attention; sound localization
The barn owl is a well-known model system for studying auditory processing and sound localization. This article reviews the morphological and functional organization, as well as the role of the underlying microcircuits, of the barn owl's inferior colliculus (IC). We focus on the processing of frequency and interaural time (ITD) and level differences (ILD). We first summarize the morphology of the sub-nuclei belonging to the IC and their differentiation by antero- and retrograde labeling and by staining with various antibodies. We then focus on the response properties of neurons in the three major sub-nuclei of IC [core of the central nucleus of the IC (ICCc), lateral shell of the central nucleus of the IC (ICCls), and the external nucleus of the IC (ICX)]. ICCc projects to ICCls, which in turn sends its information to ICX. The responses of neurons in ICCc are sensitive to changes in ITD but not to changes in ILD. The distribution of ITD sensitivity with frequency in ICCc can only partly be explained by optimal coding. We continue with the tuning properties of ICCls neurons, the first station in the midbrain where the ITD and ILD pathways merge after they have split at the level of the cochlear nucleus. The ICCc and ICCls share similar ITD and frequency tuning. By contrast, ICCls shows sigmoidal ILD tuning which is absent in ICCc. Both ICCc and ICCls project to the forebrain, and ICCls also projects to ICX, where space-specific neurons are found. Space-specific neurons exhibit side peak suppression in ITD tuning, bell-shaped ILD tuning, and are broadly tuned to frequency. These neurons respond only to restricted positions of auditory space and form a map of two-dimensional auditory space. Finally, we briefly review major IC features, including multiplication-like computations, correlates of echo suppression, plasticity, and adaptation.
sound localization; central nucleus of the inferior colliculus; auditory; plasticity; adaptation; interaural time difference; interaural level difference; frequency tuning
The KCNC1 (previously Kv3.1) potassium channel, a delayed rectifier with a high threshold of activation, is highly expressed in the time coding nuclei of the adult chicken and barn owl auditory brainstem. The proposed role of KCNC1 currents in auditory neurons is to reduce the width of the action potential and enable neurons to transmit high frequency temporal information with little jitter. Because developmental changes in potassium currents are critical for the maturation of the shape of the action potential, we used immunohistochemical methods to examine the developmental expression of KCNC1 subunits in the avian auditory brainstem. The KCNC1 gene gives rise to two splice variants, a longer KCNC1b and a shorter KCNC1a that differ at the carboxy termini. Two antibodies were used: an antibody to the N-terminus that does not distinguish between KCNC1a and b isoforms, denoted as panKCNC1, and another antibody that specifically recognizes the C terminus of KCNC1b. A comparison of the staining patterns observed with the pan-KCNC1 and the KCNC1b specific antibodies suggests that KCNC1a and KCNC1b splice variants are differentially regulated during development. Although pan-KCNC1 immunoreactivity is observed from the earliest time examined in the chicken (E10), a subcellular redistribution of the immunoproduct was apparent over the course of development. KCNC1b specific staining has a late onset with immunostaining first appearing in the regions that map high frequencies in nucleus magnocellularis (NM) and nucleus laminaris (NL). The expression of KCNC1b protein begins around E14 in the chicken and after E21 in the barn owl, relatively late during ontogeny and at the time that synaptic connections mature morphologically and functionally.
chicken; barn owl; ontogeny; time coding; outward current; high threshold
When sound arrives at the eardrum it has already been filtered by the body, head, and outer ear. This process is mathematically described by the head-related transfer functions (HRTFs), which are characteristic for the spatial position of a sound source and for the individual ear. HRTFs in the barn owl (Tyto alba) are also shaped by the facial ruff, a specialization that alters interaural time differences (ITD), interaural intensity differences (ILD), and the frequency spectrum of the incoming sound to improve sound localization. Here we created novel stimuli to simulate the removal of the barn owl's ruff in a virtual acoustic environment, thus creating a situation similar to passive listening in other animals, and used these stimuli in behavioral tests.
HRTFs were recorded from an owl before and after removal of the ruff feathers. Normal and ruff-removed conditions were created by filtering broadband noise with the HRTFs. Under normal virtual conditions, no differences in azimuthal head-turning behavior between individualized and non-individualized HRTFs were observed. The owls were able to respond differently to stimuli from the back than to stimuli from the front having the same ITD. By contrast, such a discrimination was not possible after the virtual removal of the ruff. Elevational head-turn angles were (slightly) smaller with non-individualized than with individualized HRTFs. The removal of the ruff resulted in a large decrease in elevational head-turning amplitudes.
The facial ruff a) improves azimuthal sound localization by increasing the ITD range and b) improves elevational sound localization in the frontal field by introducing a shift of iso–ILD lines out of the midsagittal plane, which causes ILDs to increase with increasing stimulus elevation. The changes at the behavioral level could be related to the changes in the binaural physical parameters that occurred after the virtual removal of the ruff. These data provide new insights into the function of external hearing structures and open up the possibility to apply the results on autonomous agents, creation of virtual auditory environments for humans, or in hearing aids.
In order to localize sounds in the environment, the auditory system detects and encodes differences in signals between each ear. The exquisite sensitivity of auditory brain stem neurons to the differences in rise time of the excitation signals from the two ears allows for neuronal encoding of microsecond interaural time differences.
Low-frequency sound localization depends on the neural computation of interaural time differences (ITD) and relies on neurons in the auditory brain stem that integrate synaptic inputs delivered by the ipsi- and contralateral auditory pathways that start at the two ears. The first auditory neurons that respond selectively to ITD are found in the medial superior olivary nucleus (MSO). We identified a new mechanism for ITD coding using a brain slice preparation that preserves the binaural inputs to the MSO. There was an internal latency difference for the two excitatory pathways that would, if left uncompensated, position the ITD response function too far outside the physiological range to be useful for estimating ITD. We demonstrate, and support using a biophysically based computational model, that a bilateral asymmetry in excitatory post-synaptic potential (EPSP) slopes provides a robust compensatory delay mechanism due to differential activation of low threshold potassium conductance on these inputs and permits MSO neurons to encode physiological ITDs. We suggest, more generally, that the dependence of spike probability on rate of depolarization, as in these auditory neurons, provides a mechanism for temporal order discrimination between EPSPs.
Animals can locate the source of a sound by detecting microsecond differences in the arrival time of sound at the two ears. Neurons encoding these interaural time differences (ITDs) receive an excitatory synaptic input from each ear. They can perform a microsecond computation with excitatory synapses that have millisecond time scale because they are extremely sensitive to the input's “rise time,” the time taken to reach the peak of the synaptic input. Current theories assume that the biophysical properties of the two inputs are identical. We challenge this assumption by showing that the rise times of excitatory synaptic potentials driven by the ipsilateral ear are faster than those driven by the contralateral ear. Further, we present a computational model demonstrating that this disparity in rise times, together with the neurons' sensitivity to excitation's rise time, can endow ITD-encoding with microsecond resolution in the biologically relevant range. Our analysis also resolves a timing mismatch. The difference between contralateral and ipsilateral latencies is substantially larger than the relevant ITD range. We show how the rise time disparity compensates for this mismatch. Generalizing, we suggest that phasic-firing neurons—those that respond to rapidly, but not to slowly, changing stimuli—are selective to the temporal ordering of brief inputs. In a coincidence-detection computation the neuron will respond more robustly when a faster input leads a slower one, even if the inputs are brief and have similar amplitudes.
Interaural time difference (ITD), or the difference in timing of a sound wave arriving at the two ears, is a fundamental cue for sound localization. A wide variety of animals have specialized neural circuits dedicated to the computation of ITDs. In the avian auditory brainstem, ITDs are encoded as the spike rates in the coincidence detector neurons of the nucleus laminaris (NL). NL neurons compare the binaural phase-locked inputs from the axons of ipsi- and contralateral nucleus magnocellularis (NM) neurons. Intracellular recordings from the barn owl's NL in vivo showed that tonal stimuli induce oscillations in the membrane potential. Since this oscillatory potential resembled the stimulus sound waveform, it was named the sound analog potential (Funabiki et al., 2011). Previous modeling studies suggested that a convergence of phase-locked spikes from NM leads to an oscillatory membrane potential in NL, but how presynaptic, synaptic, and postsynaptic factors affect the formation of the sound analog potential remains to be investigated. In the accompanying paper, we derive analytical relations between these parameters and the signal and noise components of the oscillation. In this paper, we focus on the effects of the number of presynaptic NM fibers, the mean firing rate of these fibers, their average degree of phase-locking, and the synaptic time scale. Theoretical analyses and numerical simulations show that, provided the total synaptic input is kept constant, changes in the number and spike rate of NM fibers alter the ITD-independent noise whereas the degree of phase-locking is linearly converted to the ITD-dependent signal component of the sound analog potential. The synaptic time constant affects the signal more prominently than the noise, making faster synaptic input more suitable for effective ITD computation.
phase-locking; sound localization; auditory brainstem; periodic signals; oscillation; owl
Spatial receptive fields of neurons in the auditory pathway of the barn owl result from the sensitivity to combinations of interaural time (ITD) and level differences across stimulus frequency. Both the forebrain and tectum of the owl contain such neurons. The neural pathways, which lead to the forebrain and tectal representations of auditory space, separate before the midbrain map of auditory space is synthesized. The first nuclei that belong exclusively to either the forebrain or the tectal pathways are the nucleus ovoidalis (Ov) and the external nucleus of the inferior colliculus (ICx), respectively. Both receive projections from the lateral shell subdivision of the inferior colliculus but are not interconnected. Previous studies indicate that the owl’s tectal representation of auditory space is different from those found in the owl’s forebrain and the mammalian brain. We addressed the question of whether the computation of spatial cues in both pathways is the same by comparing the ITD tuning of Ov and ICx neurons. Unlike in ICx, the relationship between frequency and ITD tuning had not been studied in single Ov units. In contrast to the conspicuous frequency independent ITD tuning of space-specific neurons of ICx, ITD selectivity varied with frequency in Ov. We also observed that the spatially tuned neurons of Ov respond to lower frequencies and are more broadly tuned to ITD than in ICx. Thus there are differences in the integration of frequency and ITD in the two sound-localization pathways. Thalamic neurons integrate spatial information not only within a broader frequency band but also across ITD channels.
Animals, including humans, use interaural time differences (ITDs) that arise from different sound path lengths to the two ears as a cue of horizontal sound source location. The nature of the neural code for ITD is still controversial. Current models differentiate between two population codes: either a map-like rate-place code of ITD along an array of neurons, consistent with a large body of data in the barn owl, or a population rate code, consistent with data from small mammals. Recently, it was proposed that these different codes reflect optimal coding strategies that depend on head size and sound frequency. The chicken makes an excellent test case of this proposal because its physical pre-requisites are similar to small mammals, yet it shares a more recent common ancestry with the owl. We show here that, like in the barn owl, the brainstem nucleus laminaris in mature chickens displayed the major features of a place code of ITD. ITD was topographically represented in the maximal responses of neurons along each isofrequency band, covering approximately the contralateral acoustic hemisphere. Furthermore, the represented ITD range appeared to change with frequency, consistent with a pressure gradient receiver mechanism in the avian middle ear. At very low frequencies, below400 Hz, maximal neural responses were symmetrically distributed around zero ITD and it remained unclear whether there was a topographic representation. These findings do not agree with the above predictions for optimal coding and thus revive the discussion as to what determines the neural coding strategies for ITDs.
Auditory; Hearing; Sound localization; Sensory
Sound localization requires comparison between the inputs to the left and right ears. One important aspect of this comparison is the differences in arrival time to each side, also called interaural time difference (ITD).A prevalent model of ITD detection, consisting of delay lines and coincidence-detector neurons, was proposed by Jeffress (J Comp Physiol Psychol 41:35–39, 1948). As an extension of the Jeffress model, the process of detecting and encoding ITD has been compared to an effective cross-correlation between the input signals to the two ears. Because the cochlea performs a spectrotemporal decomposition of the input signal, this cross-correlation takes place over narrow frequency bands. Since the cochlear tonotopy is arranged in series, sounds of different frequencies will trigger neural activity with different temporal delays. Thus, the matching of the frequency tuning of the left and right inputs to the cross-correlator units becomes a ‘timing’ issue. These properties of auditory transduction gave theoretical support to an alternative model of ITD-detection based on a bilateral mismatch in frequency tuning, called the ‘stereausis’ model. Here we first review the current literature on the owl’s nucleus laminaris, the equivalent to the medial superior olive of mammals, which is the site where ITD is detected. Subsequently, we use reverse correlation analysis and stimulation with uncorrelated sounds to extract the effective monaural inputs to the cross-correlator neurons. We show that when the left and right inputs to the cross-correlators are defined in this manner, the computation performed by coincidence-detector neurons satisfies conditions of cross-correlation theory. We also show that the spectra of left and right inputs are matched, which is consistent with predictions made by the classic model put forth by Jeffress.
Barn owl; Interaural time difference; Cross-correlation; Coincidence detection; Cochlear delays; Sound localization; Nucleus laminaris; Stereausis
Barn owls integrate spatial information across frequency channels to localize sounds in space.
We presented barn owls with synchronous sounds that contained different bands of frequencies (3–5 kHz and 7–9 kHz) from different locations in space. When the owls were confronted with the conflicting localization cues from two synchronous sounds of equal level, their orienting responses were dominated by one of the sounds: they oriented toward the location of the low frequency sound when the sources were separated in azimuth; in contrast, they oriented toward the location of the high frequency sound when the sources were separated in elevation. We identified neural correlates of this behavioral effect in the optic tectum (OT, superior colliculus in mammals), which contains a map of auditory space and is involved in generating orienting movements to sounds. We found that low frequency cues dominate the representation of sound azimuth in the OT space map, whereas high frequency cues dominate the representation of sound elevation.
We argue that the dominance hierarchy of localization cues reflects several factors: 1) the relative amplitude of the sound providing the cue, 2) the resolution with which the auditory system measures the value of a cue, and 3) the spatial ambiguity in interpreting the cue. These same factors may contribute to the relative weighting of sound localization cues in other species, including humans.
The activity of sensory neural populations carries information about the environment. This may be extracted from neural activity using different strategies. In the auditory brainstem, a recent theory proposes that sound location in the horizontal plane is decoded from the relative summed activity of two populations in each hemisphere, whereas earlier theories hypothesized that the location was decoded from the identity of the most active cells. We tested the performance of various decoders of neural responses in increasingly complex acoustical situations, including spectrum variations, noise, and sound diffraction. We demonstrate that there is insufficient information in the pooled activity of each hemisphere to estimate sound direction in a reliable way consistent with behavior, whereas robust estimates can be obtained from neural activity by taking into account the heterogeneous tuning of cells. These estimates can still be obtained when only contralateral neural responses are used, consistently with unilateral lesion studies.
Having two ears allows animals to localize the source of a sound. For example, barn owls can snatch their prey in complete darkness by relying on sound alone. It has been known for a long time that this ability depends on tiny differences in the sounds that arrive at each ear, including differences in the time of arrival: in humans, for example, sound will arrive at the ear closer to the source up to half a millisecond earlier than it arrives at the other ear. These differences are called interaural time differences. However, the way that the brain processes this information to figure out where the sound came from has been the source of much debate.
Several theories have been proposed for how the brain calculates position from interaural time differences. According to the hemispheric theory, the activities of particular binaurally sensitive neurons in each of side of the brain are added together: adding signals in this way has been shown to maximize sensitivity to time differences under simple, controlled circumstances. The peak decoding theory proposes that the brain can work out the location of a sound on the basis of which neurons responded most strongly to the sound.
Both theories have their potential advantages, and there is evidence in support of each. Now, Goodman et al. have used computational simulations to compare the models under ecologically relevant circumstances. The simulations show that the results predicted by both models are inconsistent with those observed in real animals, and they propose that the brain must use the full pattern of neural responses to calculate the location of a sound.
One of the parts of the brain that is responsible for locating sounds is the inferior colliculus. Studies in cats and humans have shown that damage to the inferior colliculus on one side of the brain prevents accurate localization of sounds on the opposite side of the body, but the animals are still able to locate sounds on the same side. This finding is difficult to explain using the hemispheric model, but Goodman et al. show that it can be explained with pattern-based models.
sound localization; neural coding; audition; None
A wide variety of neurons encode temporal information via phase-locked spikes. In the avian auditory brainstem, neurons in the cochlear nucleus magnocellularis (NM) send phase-locked synaptic inputs to coincidence detector neurons in the nucleus laminaris (NL) that mediate sound localization. Previous modeling studies suggested that converging phase-locked synaptic inputs may give rise to a periodic oscillation in the membrane potential of their target neuron. Recent physiological recordings in vivo revealed that owl NL neurons changed their spike rates almost linearly with the amplitude of this oscillatory potential. The oscillatory potential was termed the sound analog potential, because of its resemblance to the waveform of the stimulus tone. The amplitude of the sound analog potential recorded in NL varied systematically with the interaural time difference (ITD), which is one of the most important cues for sound localization. In order to investigate the mechanisms underlying ITD computation in the NM-NL circuit, we provide detailed theoretical descriptions of how phase-locked inputs form oscillating membrane potentials. We derive analytical expressions that relate presynaptic, synaptic, and postsynaptic factors to the signal and noise components of the oscillation in both the synaptic conductance and the membrane potential. Numerical simulations demonstrate the validity of the theoretical formulations for the entire frequency ranges tested (1–8 kHz) and potential effects of higher harmonics on NL neurons with low best frequencies (<2 kHz).
phase-locking; sound localization; auditory brainstem; periodic signals; oscillation; owl
Interaural time differences (ITDs) are a main cue for sound localization and sound segregation. A dominant model to study ITD detection is the sound localization circuitry in the avian auditory brainstem. Neurons in nucleus laminaris (NL) receive auditory information from both ears via the avian cochlear nucleus magnocellularis (NM) and compare the relative timing of these inputs. Timing of these inputs is crucial, as ITDs in the microsecond range must be discriminated and encoded. We modeled ITD sensitivity of single NL neurons based on previously published data and determined the minimum resolvable ITD for neurons in NL. The minimum resolvable ITD is too large to allow for discrimination by single NL neurons of naturally occurring ITDs for very low frequencies. For high frequency NL neurons (>1 kHz) our calculated ITD resolutions fall well within the natural range of ITDs and approach values of below 10 μs. We show that different parts of the ITD tuning function offer different resolution in ITD coding, suggesting that information derived from both parts may be used for downstream processing. A place code may be used for sound location at frequencies above 500 Hz, but our data suggest the slope of the ITD tuning curve ought to be used for ITD discrimination by single NL neurons at the lowest frequencies. Our results provide an important measure of the necessary temporal window of binaural inputs for future studies on the mechanisms and development of neuronal computation of temporally precise information in this important system. In particular, our data establish the temporal precision needed for conduction time regulation along NM axons.
sound localization; interaural time differences; avian brainstem; nucleus laminaris; ITD resolution
In the brainstem, the auditory system diverges into two pathways that process different sound localization cues, interaural time differences (ITDs) and level differences (ILDs). We investigated the site where ILD is detected in the auditory system of barn owls, the posterior part of the lateral lemniscus (LLDp). This structure is equivalent to the lateral superior olive in mammals. The LLDp is unique in that it is the first place of binaural convergence in the brainstem where monaural excitatory and inhibitory inputs converge. Using binaurally uncorrelated noise and a generalized linear model, we were able to estimate the spectrotemporal tuning of excitatory and inhibitory inputs to these cells. We show that the response of LLDp neurons is highly locked to the stimulus envelope. Our data demonstrate that spectrotemporally tuned, temporally delayed inhibition enhances the reliability of envelope locking by modulating the gain of LLDp neurons' responses. The dependence of gain modulation on ILD shown here constitutes a means for space-dependent coding of stimulus identity by the initial stages of the auditory pathway.
Sound information is encoded as a series of spikes of the auditory nerve fibers (ANFs), and then transmitted to the brainstem auditory nuclei. Features such as timing and level are extracted from ANFs activity and further processed as the interaural time difference (ITD) and the interaural level difference (ILD), respectively. These two interaural difference cues are used for sound source localization by behaving animals. Both cues depend on the head size of animals and are extremely small, requiring specialized neural properties in order to process these cues with precision. Moreover, the sound level and timing cues are not processed independently from one another. Neurons in the nucleus angularis (NA) are specialized for coding sound level information in birds and the ILD is processed in the posterior part of the dorsal lateral lemniscus nucleus (LLDp). Processing of ILD is affected by the phase difference of binaural sound. Temporal features of sound are encoded in the pathway starting in nucleus magnocellularis (NM), and ITD is processed in the nucleus laminaris (NL). In this pathway a variety of specializations are found in synapse morphology, neuronal excitability, distribution of ion channels and receptors along the tonotopic axis, which reduces spike timing fluctuation in the ANFs-NM synapse, and imparts precise and stable ITD processing to the NL. Moreover, the contrast of ITD processing in NL is enhanced over a wide range of sound level through the activity of GABAergic inhibitory systems from both the superior olivary nucleus (SON) and local inhibitory neurons that follow monosynaptic to NM activity.
brainstem auditory nucleus; interaural difference cues; SON; tonic inhibition; phasic inhibition
The subjective representation of the sounds delivered to the two ears of a human listener is closely associated with the interaural delay and correlation of these two-ear sounds. When the two-ear sounds, e.g., arbitrary noises, arrive simultaneously, the single auditory image of the binaurally identical noises becomes increasingly diffuse, and eventually separates into two auditory images as the interaural correlation decreases. When the interaural delay increases from zero to several milliseconds, the auditory image of the binaurally identical noises also changes from a single image to two distinct images. However, measuring the effect of these two factors on an identical group of participants has not been investigated. This study examined the impacts of interaural correlation and delay on detecting a binaurally uncorrelated fragment (interaural correlation = 0) embedded in the binaurally correlated noises (i.e., binaural gap or break in interaural correlation). We found that the minimum duration of the binaural gap for its detection (i.e., duration threshold) increased exponentially as the interaural delay between the binaurally identical noises increased linearly from 0 to 8 ms. When no interaural delay was introduced, the duration threshold also increased exponentially as the interaural correlation of the binaurally correlated noises decreased linearly from 1 to 0.4. A linear relationship between the effect of interaural delay and that of interaural correlation was described for listeners participating in this study: a 1 ms increase in interaural delay appeared to correspond to a 0.07 decrease in interaural correlation specific to raising the duration threshold. Our results imply that a tradeoff may exist between the impacts of interaural correlation and interaural delay on the subjective representation of sounds delivered to two human ears.
A multiplicative combination of tuning to interaural time difference (ITD) and interaural level difference (ILD) contributes to the generation of spatially selective auditory neurons in the owl's midbrain. Previous analyses of multiplicative responses in the owl have not taken into consideration the frequency-dependence of ITD and ILD cues that occur under natural listening conditions. Here, we present a model for the responses of ITD- and ILD-sensitive neurons in the barn owl's inferior colliculus which satisfies constraints raised by experimental data on frequency convergence, multiplicative interaction of ITD and ILD, and response properties of afferent neurons. We propose that multiplication between ITD- and ILD-dependent signals occurs only within frequency channels and that frequency integration occurs using a linear-threshold mechanism. The model reproduces the experimentally observed nonlinear responses to ITD and ILD in the inferior colliculus, with greater accuracy than previous models. We show that linear-threshold frequency integration allows the system to represent multiple sound sources with natural sound localization cues, whereas multiplicative frequency integration does not. Nonlinear responses in the owl's inferior colliculus can thus be generated using a combination of cellular and network mechanisms, showing that multiple elements of previous theories can be combined in a single system.
Spike-based neuromorphic sensors such as retinas and cochleas, change the way in which the world is sampled. Instead of producing data sampled at a constant rate, these sensors output spikes that are asynchronous and event driven. The event-based nature of neuromorphic sensors implies a complete paradigm shift in current perception algorithms toward those that emphasize the importance of precise timing. The spikes produced by these sensors usually have a time resolution in the order of microseconds. This high temporal resolution is a crucial factor in learning tasks. It is also widely used in the field of biological neural networks. Sound localization for instance relies on detecting time lags between the two ears which, in the barn owl, reaches a temporal resolution of 5 μs. Current available neuromorphic computation platforms such as SpiNNaker often limit their users to a time resolution in the order of milliseconds that is not compatible with the asynchronous outputs of neuromorphic sensors. To overcome these limitations and allow for the exploration of new types of neuromorphic computing architectures, we introduce a novel software framework on the SpiNNaker platform. This framework allows for simulations of spiking networks and plasticity mechanisms using a completely asynchronous and event-based scheme running with a microsecond time resolution. Results on two example networks using this new implementation are presented.
SpiNNaker; neuromorphic; event-based models; microsecond; asynchronous; plasticity
Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem.
Our brain's ability to perform cognitive processes, such as object identification, problem solving, and decision making, comes from the specific connections between neurons. The neurons carry information as spikes that are transmitted to other neurons via connections with different strengths and propagation delays. Experimentally observed learning rules can modify the strengths of connections between neurons based on the timing of their spikes. The learning that occurs in neuronal networks due to these rules is thought to be vital to creating the structures necessary for different cognitive processes as well as for memory. The spiking rate of populations of neurons has been observed to oscillate at particular frequencies in various brain regions, and there is evidence that these oscillations play a role in cognition. Here, we use analytical and numerical methods to investigate the changes to the network structure caused by a specific learning rule during oscillatory neural activity. We find the conditions under which connections with propagation delays that resonate with the oscillations are strengthened relative to the other connections. We demonstrate that networks learn to oscillate more strongly to oscillations at the frequency they were presented with during learning. We discuss the possible application of these results to specific areas of the brain.
The time it takes a sound to travel from source to ear differs between the ears and creates an interaural delay. It varies systematically with spatial direction and is generally modeled as a pure time delay, independent of frequency. In acoustical recordings, we found that interaural delay varies with frequency at a fine scale. In physiological recordings of midbrain neurons sensitive to interaural delay, we found that preferred delay also varies with sound frequency. Similar observations reported earlier were not incorporated in a functional framework. We find that the frequency dependence of acoustical and physiological interaural delays are matched in key respects. This suggests that binaural neurons are tuned to acoustical features of ecological environments, rather than to fixed interaural delays. Using recordings from the nerve and brainstem we show that this tuning may emerge from neurons detecting coincidences between input fibers that are mistuned in frequency.
When you hear a sound, such as someone calling your name, it is often possible to make a good estimate of where that sound came from. If the sound came from the left, it would reach your left ear before your right ear, and vice versa if the sound originated from your right. The time that passes between the sound reaching each ear is known as the ‘interaural time difference’. Previous research has suggested that specific neurons in the brain respond to specific interaural time differences, and the brain then uses this interaural time difference to locate the sound.
Sounds come in various frequencies from high-pitched alarms to low bass tones, and how a neuron responds to interaural time differences appears to change according to the frequency of the sound being played. For example, a given neuron may respond to a 200- microsecond interaural time difference when a tone is played at a high frequency, but show no response to this time difference when the tone is played at a low frequency. To date, researchers had been unable to explain why this occurs.
Here, Benichoux et al. investigated this topic by playing a variety of sounds to anaesthetized cats. Electrodes were used to record the responses of individual neurons in the cats' brains, and the properties of the sound waves that reached the cats' ears were also recorded. These experiments revealed that the time it took a sound to travel from a location to each of the cats' ears, and consequently the interaural time difference, depended on whether it was a high-pitched or a low-pitched sound. This happened because different properties of the environment, such as the angle of the cat's head, affected specific frequencies in different ways.
As expected, the neurons' responses were also affected by sound frequency. Indeed, the neurons' behaviour mirrored that of the sound waves themselves. This shows that neurons do not, as previously thought, simply react to specific interaural differences. Instead, these neurons use both sound frequency and interaural time differences to produce a thorough approximation of the sound's location. The precise mechanisms that generate this brain adaptation to the animal's environment remain to be determined.
electrophysiology; auditory brainstem; cat; other
The nucleus laminaris of the barn owl auditory system is quite impressive, since its underlying time estimation is much better than the processing speed of the involved neurons. Since precise localization is also very important in many technical applications, this paper explores to what extent the main principles of the nucleus laminaris can be implemented in digital hardware. The first prototypical implementation yields a time resolution of about 20 ps, even though the chosen standard, low-cost device is clocked at only 85 MHz, which leads to an internal duty cycle of approximately 12 ns. In addition, this paper also explores the utility of an advanced sampling scheme, known as unfolding-in-time. It turns out that with this sampling method, the prototype can easily process input signals of up to 300 MHz, which is almost four times higher than the sampling rate.
nucleus laminaris; barn owl; high resolution time measurement; localization; FPGA