Maps of interaural time difference in the chicken’s brainstem nucleus laminaris

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 prerequisites 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, below 400 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.     

Binaural disparity cues available to the barn owl for sound localization

1. Bilateral recording of cochlear potentials was used to measure the variations in interaural time differences (ITDs) and interaural intensity differences (IIDs) as a free-field auditory stimulus was moved to different positions around a barn owl's head. 2. ITD varied smoothly with stimulus azimuth across a broad frequency range. 3. ITD varied minimally with stimulus elevation, except at extreme angles from the horizontal. 4. IID varied with both stimulus elevation and stimulus azimuth. Lower frequencies were more sensitive to variations in azimuth, whereas higher frequencies were more sensitive to variations in elevation. 5. The loci of spatial coordinates that form iso-IID contours and iso-ITD contours form a non-orthogonal grid that relates binaural disparity cues to sound location.     

Sound localization: Jeffress and beyond

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

Adaptation in sound localization processing induced by interaural time difference in amplitude envelope at high frequencies

Background

When a second sound follows a long first sound, its location appears to be perceived away from the first one (the localization/lateralization aftereffect). This aftereffect has often been considered to reflect an efficient neural coding of sound locations in the auditory system. To understand determinants of the localization aftereffect, the current study examined whether it is induced by an interaural temporal difference (ITD) in the amplitude envelope of high frequency transposed tones (over 2 kHz), which is known to function as a sound localization cue.

Methodology/Principal Findings

In Experiment 1, participants were required to adjust the position of a pointer to the perceived location of test stimuli before and after adaptation. Test and adapter stimuli were amplitude modulated (AM) sounds presented at high frequencies and their positional differences were manipulated solely by the envelope ITD. Results showed that the adapter''s ITD systematically affected the perceived position of test sounds to the directions expected from the localization/lateralization aftereffect when the adapter was presented at ±600 µs ITD; a corresponding significant effect was not observed for a 0 µs ITD adapter. In Experiment 2, the observed adapter effect was confirmed using a forced-choice task. It was also found that adaptation to the AM sounds at high frequencies did not significantly change the perceived position of pure-tone test stimuli in the low frequency region (128 and 256 Hz).

Conclusions/Significance

The findings in the current study indicate that ITD in the envelope at high frequencies induces the localization aftereffect. This suggests that ITD in the high frequency region is involved in adaptive plasticity of auditory localization processing.     

Asymmetric Excitatory Synaptic Dynamics Underlie Interaural Time Difference Processing in the Auditory System

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.     

Frequency-invariant representation of interaural time differences in mammals

Interaural time differences (ITDs) are the major cue for localizing low-frequency sounds. The activity of neuronal populations in the brainstem encodes ITDs with an exquisite temporal acuity of about 10 μs. The response of single neurons, however, also changes with other stimulus properties like the spectral composition of sound. The influence of stimulus frequency is very different across neurons and thus it is unclear how ITDs are encoded independently of stimulus frequency by populations of neurons. Here we fitted a statistical model to single-cell rate responses of the dorsal nucleus of the lateral lemniscus. The model was used to evaluate the impact of single-cell response characteristics on the frequency-invariant mutual information between rate response and ITD. We found a rough correspondence between the measured cell characteristics and those predicted by computing mutual information. Furthermore, we studied two readout mechanisms, a linear classifier and a two-channel rate difference decoder. The latter turned out to be better suited to decode the population patterns obtained from the fitted model.     

Sequence learning recodes cortical representations instead of strengthening initial ones

We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patterns from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.     

Stochastic model shows how cochlear implants process azimuth in real auditory space

Interaural time difference (ITD) is a major cue for sound azimuth localization at lower sound frequencies. We review two theories of how the sound localization neural circuit works. One of them proposes labeling of sound direction in the array of delay lines by maximal response of the tuning curve (Jeffress model). The other proposes detection of the direction by calculating the maximum slope of tuning curves. We formulate a simple hypothesis from this that stochastic neural response infers sound direction from this maximum slope, which supports the second theory. We calculate the output spike time density used in the readout of sound direction analytically. We show that the numerical implementation of the model yields results similar to those observed in experiments in mammals. We then go one step further and show that our model also gives similar results when a detailed implementation of the cochlear implant processor and simulation of implant to auditory nerve transduction are used, instead of the simplified model of auditory nerve input. Our results are useful in explaining some recent puzzling observations on the binaural cochlear implantees.     

Spatial constancy and the brain: insights from neural networks

To form an accurate internal representation of visual space, the brain must accurately account for movements of the eyes, head or body. Updating of internal representations in response to these movements is especially important when remembering spatial information, such as the location of an object, since the brain must rely on non-visual extra-retinal signals to compensate for self-generated movements. We investigated the computations underlying spatial updating by constructing a recurrent neural network model to store and update a spatial location based on a gaze shift signal, and to do so flexibly based on a contextual cue. We observed a striking similarity between the patterns of behaviour produced by the model and monkeys trained to perform the same task, as well as between the hidden units of the model and neurons in the lateral intraparietal area (LIP). In this report, we describe the similarities between the model and single unit physiology to illustrate the usefulness of neural networks as a tool for understanding specific computations performed by the brain.     

Reassessing mechanisms of low-frequency sound localisation

Interaural time differences (ITDs) are the dominant cues for human localisation of low-frequency sounds. Although a mechanism for ITD processing proposed in 1948 seems applicable to birds, and is consistent with many aspects of the responses found in mammals, recent data suggest that key tenets of the model might need to be reconsidered. The model requires, at every frequency, a distribution of cells with firing rate peaks across all ITD values within the animal's physiological range. The ITD tuning relies on internal delays in the form of a neural delay line. The evidence for such a delay line structure in mammals is not as convincing as it is in birds and, in some small animals the full range of physiological ITDs are not fully represented by peak firing of neurones at every frequency channel. Alternative means of achieving internal delays such as inhibitory inputs or the delays associated with cochlear filtering are being considered.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

A Dominance Hierarchy of Auditory Spatial Cues in Barn Owls

Background

Barn owls integrate spatial information across frequency channels to localize sounds in space.

Methodology/Principal Findings

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.

Conclusions/Significance

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.     

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

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

The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information

In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex.     

On the Origins of Suboptimality in Human Probabilistic Inference

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects'' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects'' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.     

A continuous-time neural model for sequential action

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.     

Shifting attention within memory representations involves early visual areas

Prior studies have shown that spatial attention modulates early visual cortex retinotopically, resulting in enhanced processing of external perceptual representations. However, it is not clear whether the same visual areas are modulated when attention is focused on, and shifted within a working memory representation. In the current fMRI study participants were asked to memorize an array containing four stimuli. After a delay, participants were presented with a verbal cue instructing them to actively maintain the location of one of the stimuli in working memory. Additionally, on a number of trials a second verbal cue instructed participants to switch attention to the location of another stimulus within the memorized representation. Results of the study showed that changes in the BOLD pattern closely followed the locus of attention within the working memory representation. A decrease in BOLD-activity (V1-V3) was observed at ROIs coding a memory location when participants switched away from this location, whereas an increase was observed when participants switched towards this location. Continuous increased activity was obtained at the memorized location when participants did not switch. This study shows that shifting attention within memory representations activates the earliest parts of visual cortex (including V1) in a retinotopic fashion. We conclude that even in the absence of visual stimulation, early visual areas support shifting of attention within memorized representations, similar to when attention is shifted in the outside world. The relationship between visual working memory and visual mental imagery is discussed in light of the current findings.     

On the ability of neurons in the barn owl's inferior colliculus to sense brief appearances of interaural time difference

Summary This paper investigates the ability of neurons in the barn owl's (Tyto alba) inferior colliculus to sense brief appearances of interaural time difference (ITD), the main cue for azimuthal sound localization in this species. In the experiments, ITD-tuning was measured during presentation of a mask-probe-mask sequence. The probe consisted of a noise having a constant ITD, while the mask consisted of binaurally uncorrelated noise. Collicular neurons discriminated between the probe and masking noise by showing rapid changes from untuned to tuned and back to untuned responses.The curve describing the relation between probe duration and the degree of ITD-tuning resembled a leaky-integration process with a time constant of about 2 ms. Many neurons were ITD-tuned when probe duration was below 1 ms. These extremely short effective probe durations are interpreted as evidence for neuronal convergence within the pathway computing ITD. The minimal probe duration necessary for ITD-tuning was independent of the bandwidth of the neurons' frequency tuning and also of the best frequency of a neuron. Many narrowly tuned neurons having different best frequencies converge to form a broad-band neuron. To yield the short effective probe durations the convergence must occur in strong temporal synchronism.Abbreviations ICc central nucleus of the inferior colliculus; - ICx external nucleus of the inferior colliculus; - ITD interaural time difference - LP Likelihood parameter     

A spike-based model of binaural sound localization

This paper describes a spike-based model of binaural sound localization using interaural time differences (ITDs). To handle the problem of temporal coding and to facilitate a hardware implementation all neurons are simulated by a spike response model, which includes postsynaptic potentials (PSPs) and a refractory period. A winner-take-all (WTA) network selects the dominant source from the representation of the sound's angles of incidences, and can be biased by a multisensory support. We use simulations on real audio data to investigate the function and the practical application of the system.