Diverse processing underlying frequency integration in midbrain neurons of barn owls

Emergent response properties of sensory neurons depend on circuit connectivity and somatodendritic processing. Neurons of the barn owl’s external nucleus of the inferior colliculus (ICx) display emergence of spatial selectivity. These neurons use interaural time difference (ITD) as a cue for the horizontal direction of sound sources. ITD is detected by upstream brainstem neurons with narrow frequency tuning, resulting in spatially ambiguous responses. This spatial ambiguity is resolved by ICx neurons integrating inputs over frequency, a relevant processing in sound localization across species. Previous models have predicted that ICx neurons function as point neurons that linearly integrate inputs across frequency. However, the complex dendritic trees and spines of ICx neurons raises the question of whether this prediction is accurate. Data from in vivo intracellular recordings of ICx neurons were used to address this question. Results revealed diverse frequency integration properties, where some ICx neurons showed responses consistent with the point neuron hypothesis and others with nonlinear dendritic integration. Modeling showed that varied connectivity patterns and forms of dendritic processing may underlie observed ICx neurons’ frequency integration processing. These results corroborate the ability of neurons with complex dendritic trees to implement diverse linear and nonlinear integration of synaptic inputs, of relevance for adaptive coding and learning, and supporting a fundamental mechanism in sound localization.     

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.     

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter—describing somatic integration—and the spike-history filter—accounting for spike-frequency adaptation—dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na⁺-channel inactivation regulate the sensitivity to rapid input fluctuations.     

High-Fidelity Coding with Correlated Neurons

Positive correlations in the activity of neurons are widely observed in the brain. Previous studies have shown these correlations to be detrimental to the fidelity of population codes, or at best marginally favorable compared to independent codes. Here, we show that positive correlations can enhance coding performance by astronomical factors. Specifically, the probability of discrimination error can be suppressed by many orders of magnitude. Likewise, the number of stimuli encoded—the capacity—can be enhanced more than tenfold. These effects do not necessitate unrealistic correlation values, and can occur for populations with a few tens of neurons. We further show that both effects benefit from heterogeneity commonly seen in population activity. Error suppression and capacity enhancement rest upon a pattern of correlation. Tuning of one or several effective parameters can yield a limit of perfect coding: the corresponding pattern of positive correlation leads to a ‘lock-in’ of response probabilities that eliminates variability in the subspace relevant for stimulus discrimination. We discuss the nature of this pattern and we suggest experimental tests to identify it.     

The acoustic behaviour of the bushcricket Tettigonia cantans: III. Coprocessing of auditory and vibratory information in the central nervous system

All the acoustic units in the ventral-nerve cord respond to both sound and vibration. Most of them show improved coding abilities when stimulated simultaneously with conspecific songs and vibration signals. This is also true for habituating neurons. Stridulating tettigoniids produce both airborne sound and substrate borne vibration and their simultaneous processing in the central nervous system may therefore lead to a better localization of a nearly sound source in the biotope.     

Probing real sensory worlds of receivers with unsupervised clustering

The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.     

Characteristics of unit responses of the cat cerebellar cortex to acoustic stimuli

Investigation of unit responses of the cerebellar cortex (lobules VI–VII of the vermis) to acoustic stimulation showed that the great majority of neurons responded by a discharge of one spike or a group of spikes with a latent period of 10–40 msec and with a low fluctuation value. Neurons identified as Purkinje cells responded to sound either by inhibition of spontaneous activity or by a "climbing fiber response" with a latent period of 40–60 msec and with a high fluctuation value. In 4 of 80 neurons a prolonged (lasting about 1 sec or more), variable response with a latent period of 225–580 msec was observed. The minimal thresholds of unit responses to acoustic stimuli were distributed within the range from –7 to 77 dB, with a mode from 20 to 50 dB. All the characteristics of the cerebellar unit responses studied were independent of the intensity, duration, and frequency of the sound, like neurons of short-latency type in the inferior colliculi. In certain properties — firing pattern, latent period, and threshold of response — the cerebellar neurons resemble neurons of higher levels of the auditory system: the medial geniculate body and auditory cortex.I. P. Pavlov Institute of Physiology, Academy of Sciences of the USSR, Leningrad. Translated from Neirofiziologiya, Vol. 5, No. 1, pp. 3–12, January–February, 1973.     

The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds

In mammalian auditory cortex, sound source position is represented by a population of broadly tuned neurons whose firing is modulated by sounds located at all positions surrounding the animal. Peaks of their tuning curves are concentrated at lateral position, while their slopes are steepest at the interaural midline, allowing for the maximum localization accuracy in that area. These experimental observations contradict initial assumptions that the auditory space is represented as a topographic cortical map. It has been suggested that a “panoramic” code has evolved to match specific demands of the sound localization task. This work provides evidence suggesting that properties of spatial auditory neurons identified experimentally follow from a general design principle- learning a sparse, efficient representation of natural stimuli. Natural binaural sounds were recorded and served as input to a hierarchical sparse-coding model. In the first layer, left and right ear sounds were separately encoded by a population of complex-valued basis functions which separated phase and amplitude. Both parameters are known to carry information relevant for spatial hearing. Monaural input converged in the second layer, which learned a joint representation of amplitude and interaural phase difference. Spatial selectivity of each second-layer unit was measured by exposing the model to natural sound sources recorded at different positions. Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex. This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions. Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding.     

An ideal-observer model of human sound localization

In recent years, a great deal of research within the field of sound localization has been aimed at finding the acoustic cues that human listeners use to localize sounds and understanding the mechanisms by which they process these cues. In this paper, we propose a complementary approach by constructing an ideal-observer model, by which we mean a model that performs optimal information processing within a Bayesian context. The model considers all available spatial information contained within the acoustic signals encoded by each ear. Parameters for the optimal Bayesian model are determined based on psychoacoustic discrimination experiments on interaural time difference and sound intensity. Without regard as to how the human auditory system actually processes information, we examine the best possible localization performance that could be achieved based only on analysis of the input information, given the constraints of the normal auditory system. We show that the model performance is generally in good agreement with the actual human localization performance, as assessed in a meta-analysis of many localization experiments (Best et al. in Principles and applications of spatial hearing, pp 14–23. World Scientific Publishing, Singapore, 2011). We believe this approach can shed new light on the optimality (or otherwise) of human sound localization, especially with regard to the level of uncertainty in the input information. Moreover, the proposed model allows one to study the relative importance of various (combinations of) acoustic cues for spatial localization and enables a prediction of which cues are most informative and therefore likely to be used by humans in various circumstances.     

Auditory information coding by modeled cochlear nucleus neurons

In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.     

Enhancement and Distortion in the Temporal Representation of Sounds in the Ventral Cochlear Nucleus of Chinchillas and Cats

A subset of neurons in the cochlear nucleus (CN) of the auditory brainstem has the ability to enhance the auditory nerve''s temporal representation of stimulating sounds. These neurons reside in the ventral region of the CN (VCN) and are usually known as highly synchronized, or high-sync, neurons. Most published reports about the existence and properties of high-sync neurons are based on recordings performed on a VCN output tract—not the VCN itself—of cats. In other species, comprehensive studies detailing the properties of high-sync neurons, or even acknowledging their existence, are missing.Examination of the responses of a population of VCN neurons in chinchillas revealed that a subset of those neurons have temporal properties similar to high-sync neurons in the cat. Phase locking and entrainment—the ability of a neuron to fire action potentials at a certain stimulus phase and at almost every stimulus period, respectively—have similar maximum values in cats and chinchillas. Ranges of characteristic frequencies for high-sync neurons in chinchillas and cats extend up to 600 and 1000 Hz, respectively. Enhancement of temporal processing relative to auditory nerve fibers (ANFs), which has been shown previously in cats using tonal and white-noise stimuli, is also demonstrated here in the responses of VCN neurons to synthetic and spoken vowel sounds.Along with the large amount of phase locking displayed by some VCN neurons there occurs a deterioration in the spectral representation of the stimuli (tones or vowels). High-sync neurons exhibit a greater distortion in their responses to tones or vowels than do other types of VCN neurons and auditory nerve fibers.Standard deviations of first-spike latency measured in responses of high-sync neurons are lower than similar values measured in ANFs'' responses. This might indicate a role of high-sync neurons in other tasks beyond sound localization.     

The Sign Rule and Beyond: Boundary Effects,Flexibility, and Noise Correlations in Neural Population Codes

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population''s ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all.     

Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.     

Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: I Dynamics

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II).     

Neurophysiological Aspects of Song Pattern Recognition and Sound Localization in Grasshoppers

SYNOPSIS. In their intraspecific communication females of thegrasshopper Chorthippus biguttulus are able to detect minutegaps in songs. Males of this species can lateralize sound withless than 1 dB difference between the two ears. Behavioral experimentssuggested that separate pathways exist for song recognitionand sound localization. As for the neurophysiological basis,auditory receptors respond tonically and necessarily carry allinformation explaining behavioral performances in their spikingresponses. However, for pattern recognition as well as for codingof directional information, it seems necessary for the animalto evaluate a whole set of parallel receptor fibres to achievethe precision observed in behavior. The information of receptorsconverges onto thoracic neurons which drive neurons ascendingto the brain. Some of these ascending neurons exhibit dramaticresponse differences either for various temporal patterns orfor sound from different directions and therefore may representpathways specialized for song recognition or for sound localization.     

Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that — in addition to commonly used geometric information — makes use of a novel multi–modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints.     

On the Relevance of Natural Stimuli for the Study of Brainstem Correlates: The Example of Consonance Perception

Some combinations of musical tones sound pleasing to Western listeners, and are termed consonant, while others sound discordant, and are termed dissonant. The perceptual phenomenon of consonance has been traced to the acoustic property of harmonicity. It has been repeatedly shown that neural correlates of consonance can be found as early as the auditory brainstem as reflected in the harmonicity of the scalp-recorded frequency-following response (FFR). “Neural Pitch Salience” (NPS) measured from FFRs—essentially a time-domain equivalent of the classic pattern recognition models of pitch—has been found to correlate with behavioral judgments of consonance for synthetic stimuli. Following the idea that the auditory system has evolved to process behaviorally relevant natural sounds, and in order to test the generalizability of this finding made with synthetic tones, we recorded FFRs for consonant and dissonant intervals composed of synthetic and natural stimuli. We found that NPS correlated with behavioral judgments of consonance and dissonance for synthetic but not for naturalistic sounds. These results suggest that while some form of harmonicity can be computed from the auditory brainstem response, the general percept of consonance and dissonance is not captured by this measure. It might either be represented in the brainstem in a different code (such as place code) or arise at higher levels of the auditory pathway. Our findings further illustrate the importance of using natural sounds, as a complementary tool to fully-controlled synthetic sounds, when probing auditory perception.     

Distributed coding of sound locations in the auditory cortex

Although the auditory cortex plays an important role in sound localization, that role is not well understood. In this paper, we examine the nature of spatial representation within the auditory cortex, focusing on three questions. First, are sound-source locations encoded by individual sharply tuned neurons or by activity distributed across larger neuronal populations? Second, do temporal features of neural responses carry information about sound-source location? Third, are any fields of the auditory cortex specialized for spatial processing? We present a brief review of recent work relevant to these questions along with the results of our investigations of spatial sensitivity in cat auditory cortex. Together, they strongly suggest that space is represented in a distributed manner, that response timing (notably first-spike latency) is a critical information-bearing feature of cortical responses, and that neurons in various cortical fields differ in both their degree of spatial sensitivity and their manner of spatial coding. The posterior auditory field (PAF), in particular, is well suited for the distributed coding of space and encodes sound-source locations partly by modulations of response latency. Studies of neurons recorded simultaneously from PAF and/or A1 reveal that spatial information can be decoded from the relative spike times of pairs of neurons - particularly when responses are compared between the two fields - thus partially compensating for the absence of an absolute reference to stimulus onset.     

Optimal Prediction of Moving Sound Source Direction in the Owl

Capturing nature’s statistical structure in behavioral responses is at the core of the ability to function adaptively in the environment. Bayesian statistical inference describes how sensory and prior information can be combined optimally to guide behavior. An outstanding open question of how neural coding supports Bayesian inference includes how sensory cues are optimally integrated over time. Here we address what neural response properties allow a neural system to perform Bayesian prediction, i.e., predicting where a source will be in the near future given sensory information and prior assumptions. The work here shows that the population vector decoder will perform Bayesian prediction when the receptive fields of the neurons encode the target dynamics with shifting receptive fields. We test the model using the system that underlies sound localization in barn owls. Neurons in the owl’s midbrain show shifting receptive fields for moving sources that are consistent with the predictions of the model. We predict that neural populations can be specialized to represent the statistics of dynamic stimuli to allow for a vector read-out of Bayes-optimal predictions.