Constructing Noise-Invariant Representations of Sound in the Auditory Pathway

Identifying behaviorally relevant sounds in the presence of background noise is one of the most important and poorly understood challenges faced by the auditory system. An elegant solution to this problem would be for the auditory system to represent sounds in a noise-invariant fashion. Since a major effect of background noise is to alter the statistics of the sounds reaching the ear, noise-invariant representations could be promoted by neurons adapting to stimulus statistics. Here we investigated the extent of neuronal adaptation to the mean and contrast of auditory stimulation as one ascends the auditory pathway. We measured these forms of adaptation by presenting complex synthetic and natural sounds, recording neuronal responses in the inferior colliculus and primary fields of the auditory cortex of anaesthetized ferrets, and comparing these responses with a sophisticated model of the auditory nerve. We find that the strength of both forms of adaptation increases as one ascends the auditory pathway. To investigate whether this adaptation to stimulus statistics contributes to the construction of noise-invariant sound representations, we also presented complex, natural sounds embedded in stationary noise, and used a decoding approach to assess the noise tolerance of the neuronal population code. We find that the code for complex sounds in the periphery is affected more by the addition of noise than the cortical code. We also find that noise tolerance is correlated with adaptation to stimulus statistics, so that populations that show the strongest adaptation to stimulus statistics are also the most noise-tolerant. This suggests that the increase in adaptation to sound statistics from auditory nerve to midbrain to cortex is an important stage in the construction of noise-invariant sound representations in the higher auditory brain.     

Competing sound sources reveal spatial effects in cortical processing

Why is spatial tuning in auditory cortex weak, even though location is important to object recognition in natural settings? This question continues to vex neuroscientists focused on linking physiological results to auditory perception. Here we show that the spatial locations of simultaneous, competing sound sources dramatically influence how well neural spike trains recorded from the zebra finch field L (an analog of mammalian primary auditory cortex) encode source identity. We find that the location of a birdsong played in quiet has little effect on the fidelity of the neural encoding of the song. However, when the song is presented along with a masker, spatial effects are pronounced. For each spatial configuration, a subset of neurons encodes song identity more robustly than others. As a result, competing sources from different locations dominate responses of different neural subpopulations, helping to separate neural responses into independent representations. These results help elucidate how cortical processing exploits spatial information to provide a substrate for selective spatial auditory attention.     

弱背景噪声对大鼠听皮层神经元频率调谐的影响

在自然环境中,人和动物常在一定的背景噪声下感知信号声刺激,然而,关于低强度的弱背景噪声如何影响听皮层神经元对声刺激频率的编码尚不清楚.本研究以大鼠听皮层神经元的频率反应域为研究对象,测定了阈下背景噪声对79个神经元频率反应域的影响.结果表明,弱背景噪声对大鼠初级听皮层神经元的听反应既有抑制性影响、又有易化性影响.一般来说,抑制性影响使神经元的频率调谐范围和最佳频率反应域缩小,易化性影响使神经元的频率调谐范围和最佳频率反应域增大.对于少数神经元,弱背景噪声并未显著改变其频率调谐范围,但却改变了其最佳频率反应域范围.弱背景噪声对63.64%神经元的特征频率和55.84%神经元的最低阈值无显著影响.神经元频率调谐曲线的尖部比中部更容易受到弱背景噪声的影响.该研究结果有助于我们进一步理解复杂声环境下大脑听皮层对听觉信息的编码机制.     

Modeling of localization phenomena of the auditory image caused by brain regions dysfunctions

This paper focuses on the modeling of the neural representations of subjective auditory image motion characters that can be induced by bilateral brain dysfunctions. The modeling is based on a biologically inspired computational model that simulates the behavior of bilaterally located populations of binaural auditory neurons. To assess neural model responses in terms of psychophysical data, a special method is designed. The application of the method reveals the inertness of auditory image motion perception. The results of the modeling show stable influence of both value and bilateral location of the noises (which mimic effect of different brain part dysfunctions) on phenomena of auditory image localization.     

Hierarchical representations in the auditory cortex

Understanding the neural mechanisms of invariant object recognition remains one of the major unsolved problems in neuroscience. A common solution that is thought to be employed by diverse sensory systems is to create hierarchical representations of increasing complexity and tolerance. However, in the mammalian auditory system many aspects of this hierarchical organization remain undiscovered, including the prominent classes of high-level representations (that would be analogous to face selectivity in the visual system or selectivity to bird's own song in the bird) and the dominant types of invariant transformations. Here we review the recent progress that begins to probe the hierarchy of auditory representations, and the computational approaches that can be helpful in achieving this feat.     

Processing of spectral localization-informative changes in sound signals by neurons of inferior colliculus and auditory cortex of the house mouse Mus musculus

Comparative analysis was performed of sensitivity of three populations of neurons of the inferior colliculus central nucleus and of neurons of the auditory cortex A1 and AAF fields of the house mouse Mus musculus to series of signals of wideband noise with spectral notch shifting along the frequency axis and to series of the band noise signals with shifting band. Sensitivity to spectral notches in noise was estimated from a change of impulse activity depending on notch location on the frequency axis (modulation coefficients were determined as the normalized difference between the maximal and minimal spike number in neuronal responses to all noises with notch exposed in the series). It was shown that the highest modulation coefficient values and accordingly the highest frequency-dependent sensitivity to spectral notches in the noise were peculiar to inhibition-dependent inferior colliculus neurons. Statistical analysis confirmed that distribution of modulation coefficients for the group of the inhibition-dependent neurons differed statistically significantly from the distribution for groups of primary-like and V-shaped inferior colliculus neurons as well as of cortical neurons (U-test, p < 0.0001). The lowest sensitivity to spectral notches was revealed in the V-shaped inferior colliculus neurons and cortical neurons; in these groups, distribution of modulation coefficients did not differed statistically significantly (p > 0.3). Thus, although a part of cortical neurons does have the frequency-dependent selectivity to spectral localizationally informative changes in sound signals, its formation needs participation of the inferior colliculus and its inhibition-dependent neurons. Selectivity to direction of the shift of spectral changes in noise signals in neurons of the inferior colliculus and auditory cortex was similar and was manifested mainly as shift along the frequency axis of dependences of the spike number in the neuronal responses and latent periods on central frequency of notch in noise (the noise band).     

Song- and order-selective neurons develop in the songbird anterior forebrain during vocal learning

The anterior forebrain (AF) pathway of songbirds has an essential but poorly understood function during song learning, a process requiring auditory experience. Consistent with a role in processing auditory information, two nuclei of the AF, the lateral magnocellular nucleus of the anterior neostriatum (lMAN) and Area X (X), contain some of the most complex auditory neurons known. In adult zebra finches, these neurons are strongly selective for both spectral and temporal properties of song: They respond more robustly to the bird's own song (BOS) than to songs of conspecific individuals, and they respond less well to BOS if it is played in reverse. lMAN and X neurons of young finches early in the process of song learning (30–45 days of age) are also song responsive, but lack the song and order selectivity present in adult birds. By an intermediate stage of learning (60 days), when birds have experience of both tutor song and their own developing (plastic) song, AF neurons have significant song and order selectivity for both tutor song and BOS (in this case, plastic song). The degree of BOS selectivity is still less than that found in adults, however. In addition, neurons at 60 days are heterogenous in their preference for BOS versus tutor song: Most prefer BOS, some prefer tutor song, and others respond equally to both songs. The selectivity of adult AF auditory neurons therefore arises rapidly during development from neurons that are initially unselective. These neurons are one of the clearest examples of experience-dependent acquisition of complex stimulus selectivity. Moreover, the neural selectivity for both BOS and tutor song at 60 days raises the possibility that experience of both songs during learning contributes to the properties of individual AF neurons. © 1997 John Wiley & Sons, Inc. J Neurobiol 33: 694–709, 1997     

Response variability in the mammalian auditory cortex: an objection to feature detection?

Research strategy in the auditory system has tended to parallel that in the visual system, where neurons have been shown to respond selectively to specific stimulus parameters. Auditory neurons have been shown to be sensitive to changes in acoustic parameters, but only rarely have neurons been reported that respond exclusively to only one biologically significant sound. Even at higher levels of the auditory system very few cells have been found that could be described as "vocalization detectors." In addition, variability in responses to artificial sounds have been reported for auditory cortical neurons similar to the response variability that has been reported in the visual system. Recent evidence indicates that the responses of auditory cortical neurons to species-specific vocalizations can also be labile, varying in both strength and selectivity. This is especially true of the secondary auditory cortex. This variability, coupled with the lack of extreme specificity in the secondary auditory cortex, suggests that secondary cortical neurons are not well suited for the role of "vocalization detectors."     

Learning receptive field properties of complex cells in V1

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.     

Functional MRI of auditory responses in the zebra finch forebrain reveals a hierarchical organisation based on signal strength but not selectivity

Background

Male songbirds learn their songs from an adult tutor when they are young. A network of brain nuclei known as the ‘song system’ is the likely neural substrate for sensorimotor learning and production of song, but the neural networks involved in processing the auditory feedback signals necessary for song learning and maintenance remain unknown. Determining which regions show preferential responsiveness to the bird''s own song (BOS) is of great importance because neurons sensitive to self-generated vocalisations could mediate this auditory feedback process. Neurons in the song nuclei and in a secondary auditory area, the caudal medial mesopallium (CMM), show selective responses to the BOS. The aim of the present study is to investigate the emergence of BOS selectivity within the network of primary auditory sub-regions in the avian pallium.

Methods and Findings

Using blood oxygen level-dependent (BOLD) fMRI, we investigated neural responsiveness to natural and manipulated self-generated vocalisations and compared the selectivity for BOS and conspecific song in different sub-regions of the thalamo-recipient area Field L. Zebra finch males were exposed to conspecific song, BOS and to synthetic variations on BOS that differed in spectro-temporal and/or modulation phase structure. We found significant differences in the strength of BOLD responses between regions L2a, L2b and CMM, but no inter-stimuli differences within regions. In particular, we have shown that the overall signal strength to song and synthetic variations thereof was different within two sub-regions of Field L2: zone L2a was significantly more activated compared to the adjacent sub-region L2b.

Conclusions

Based on our results we suggest that unlike nuclei in the song system, sub-regions in the primary auditory pallium do not show selectivity for the BOS, but appear to show different levels of activity with exposure to any sound according to their place in the auditory processing stream.     

Neurophysiological response selectivity for conspecific songs over synthetic sounds in the auditory forebrain of non-singing female songbirds

Female choice plays a critical role in the evolution of male acoustic displays. Yet there is limited information on the neurophysiological basis of female songbirds’ auditory recognition systems. To understand the neural mechanisms of how non-singing female songbirds perceive behaviorally relevant vocalizations, we recorded responses of single neurons to acoustic stimuli in two auditory forebrain regions, the caudal lateral mesopallium (CLM) and Field L, in anesthetized adult female zebra finches (Taeniopygia guttata). Using various metrics of response selectivity, we found consistently higher response strengths for unfamiliar conspecific songs compared to tone pips and white noise in Field L but not in CLM. We also found that neurons in the left auditory forebrain had lower response strengths to synthetics sounds, leading to overall higher neural selectivity for song in neurons of the left hemisphere. This laterality effect is consistent with previously published behavioral data in zebra finches. Overall, our results from Field L are in parallel and from CLM are in contrast with the patterns of response selectivity reported for conspecific songs over synthetic sounds in male zebra finches, suggesting some degree of sexual dimorphism of auditory perception mechanisms in songbirds.     

Neuronal selectivity to complex vocalization features emerges in the superficial layers of primary auditory cortex

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how nonselective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from nonselective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in nonselective and feature-selective populations remain open question. In this study, using unanesthetized guinea pigs (GPs), a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in 3 auditory processing stages—the thalamus (ventral medial geniculate body (vMGB)), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call selectivity with about a third of neurons responding to only 1 or 2 call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4, stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1 and set the stage for further mechanistic studies.

A study of the neuronal representations elicited in guinea pigs by conspecific calls at different auditory processing stages reveals insights into where call-selective neuronal responses emerge; the transformation from nonselective to call-selective responses occurs in the superficial layers of the primary auditory cortex.     

Cholecystokinin from the entorhinal cortex enables neural plasticity in the auditory cortex

Patients with damage to the medial temporal lobe show deficits in forming new declarative memories but can still recall older memories, suggesting that the medial temporal lobe is necessary for encoding memories in the neocortex. Here, we found that cortical projection neurons in the perirhinal and entorhinal cortices were mostly immunopositive for cholecystokinin (CCK). Local infusion of CCK in the auditory cortex of anesthetized rats induced plastic changes that enabled cortical neurons to potentiate their responses or to start responding to an auditory stimulus that was paired with a tone that robustly triggered action potentials. CCK infusion also enabled auditory neurons to start responding to a light stimulus that was paired with a noise burst. In vivo intracellular recordings in the auditory cortex showed that synaptic strength was potentiated after two pairings of presynaptic and postsynaptic activity in the presence of CCK. Infusion of a CCK_B antagonist in the auditory cortex prevented the formation of a visuo-auditory association in awake rats. Finally, activation of the entorhinal cortex potentiated neuronal responses in the auditory cortex, which was suppressed by infusion of a CCK_B antagonist. Together, these findings suggest that the medial temporal lobe influences neocortical plasticity via CCK-positive cortical projection neurons in the entorhinal cortex.     

Arrhythmic Song Exposure Increases ZENK Expression in Auditory Cortical Areas and Nucleus Taeniae of the Adult Zebra Finch

Rhythm is important in the production of motor sequences such as speech and song. Deficits in rhythm processing have been implicated in human disorders that affect speech and language processing, including stuttering, autism, and dyslexia. Songbirds provide a tractable model for studying the neural underpinnings of rhythm processing due to parallels with humans in neural structures and vocal learning patterns. In this study, adult zebra finches were exposed to naturally rhythmic conspecific song or arrhythmic song. Immunohistochemistry for the immediate early gene ZENK was used to detect neural activation in response to these two types of stimuli. ZENK was increased in response to arrhythmic song in the auditory association cortex homologs, caudomedial nidopallium (NCM) and caudomedial mesopallium (CMM), and the avian amygdala, nucleus taeniae (Tn). CMM also had greater ZENK labeling in females than males. The increased neural activity in NCM and CMM during perception of arrhythmic stimuli parallels increased activity in the human auditory cortex following exposure to unexpected, or perturbed, auditory stimuli. These auditory areas may be detecting errors in arrhythmic song when comparing it to a stored template of how conspecific song is expected to sound. CMM may also be important for females in evaluating songs of potential mates. In the context of other research in songbirds, we suggest that the increased activity in Tn may be related to the value of song for assessing mate choice and bonding or it may be related to perception of arrhythmic song as aversive.     

Differences between Spectro-Temporal Receptive Fields Derived from Artificial and Natural Stimuli in the Auditory Cortex

Spectro-temporal properties of auditory cortex neurons have been extensively studied with artificial sounds but it is still unclear whether they help in understanding neuronal responses to communication sounds. Here, we directly compared spectro-temporal receptive fields (STRFs) obtained from the same neurons using both artificial stimuli (dynamic moving ripples, DMRs) and natural stimuli (conspecific vocalizations) that were matched in terms of spectral content, average power and modulation spectrum. On a population of auditory cortex neurons exhibiting reliable tuning curves when tested with pure tones, significant STRFs were obtained for 62% of the cells with vocalizations and 68% with DMR. However, for many cells with significant vocalization-derived STRFs (STRF_voc) and DMR-derived STRFs (STRF_dmr), the BF, latency, bandwidth and global STRFs shape differed more than what would be predicted by spiking responses simulated by a linear model based on a non-homogenous Poisson process. Moreover STRF_voc predicted neural responses to vocalizations more accurately than STRF_dmr predicted neural response to DMRs, despite similar spike-timing reliability for both sets of stimuli. Cortical bursts, which potentially introduce nonlinearities in evoked responses, did not explain the differences between STRF_voc and STRF_dmr. Altogether, these results suggest that the nonlinearity of auditory cortical responses makes it difficult to predict responses to communication sounds from STRFs computed from artificial stimuli.     

Comparison of Neural Responses to Cat Meows and Human Vowels in the Anterior and Posterior Auditory Field of Awake Cats

For humans and animals, the ability to discriminate speech and conspecific vocalizations is an important physiological assignment of the auditory system. To reveal the underlying neural mechanism, many electrophysiological studies have investigated the neural responses of the auditory cortex to conspecific vocalizations in monkeys. The data suggest that vocalizations may be hierarchically processed along an anterior/ventral stream from the primary auditory cortex (A1) to the ventral prefrontal cortex. To date, the organization of vocalization processing has not been well investigated in the auditory cortex of other mammals. In this study, we examined the spike activities of single neurons in two early auditory cortical regions with different anteroposterior locations: anterior auditory field (AAF) and posterior auditory field (PAF) in awake cats, as the animals were passively listening to forward and backward conspecific calls (meows) and human vowels. We found that the neural response patterns in PAF were more complex and had longer latency than those in AAF. The selectivity for different vocalizations based on the mean firing rate was low in both AAF and PAF, and not significantly different between them; however, more vocalization information was transmitted when the temporal response profiles were considered, and the maximum transmitted information by PAF neurons was higher than that by AAF neurons. Discrimination accuracy based on the activities of an ensemble of PAF neurons was also better than that of AAF neurons. Our results suggest that AAF and PAF are similar with regard to which vocalizations they represent but differ in the way they represent these vocalizations, and there may be a complex processing stream between them.     

What is all the noise about in interval timing?

Cognitive processes such as decision-making, rate calculation and planning require an accurate estimation of durations in the supra-second range—interval timing. In addition to being accurate, interval timing is scale invariant: the time-estimation errors are proportional to the estimated duration. The origin and mechanisms of this fundamental property are unknown. We discuss the computational properties of a circuit consisting of a large number of (input) neural oscillators projecting on a small number of (output) coincidence detector neurons, which allows time to be coded by the pattern of coincidental activation of its inputs. We showed analytically and checked numerically that time-scale invariance emerges from the neural noise. In particular, we found that errors or noise during storing or retrieving information regarding the memorized criterion time produce symmetric, Gaussian-like output whose width increases linearly with the criterion time. In contrast, frequency variability produces an asymmetric, long-tailed Gaussian-like output, that also obeys scale invariant property. In this architecture, time-scale invariance depends neither on the details of the input population, nor on the distribution probability of noise.     

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.     

Resonant neurons and bushcricket behaviour

The resonant properties of the intrinsic dynamics of single neurons could play a direct role in behaviour. One plausible role is in the recognition of temporal patterns, such as that seen in the auditory communication systems of Orthoptera. Recent behavioural data from bushcrickets suggests that this behaviour has interesting resonance properties, but the underlying mechanism is unknown. Here we show that a very simple and general model for neural resonance could directly account for the different behavioural responses of bushcrickets to different song patterns.