Quantitative characterisation procedure for auditory neurons based on the spectro-temporal receptive field

Neural Encoding of Auditory Features during Music Perception and Imagery

Despite many behavioral and neuroimaging investigations, it remains unclear how the human cortex represents spectrotemporal sound features during auditory imagery, and how this representation compares to auditory perception. To assess this, we recorded electrocorticographic signals from an epileptic patient with proficient music ability in 2 conditions. First, the participant played 2 piano pieces on an electronic piano with the sound volume of the digital keyboard on. Second, the participant replayed the same piano pieces, but without auditory feedback, and the participant was asked to imagine hearing the music in his mind. In both conditions, the sound output of the keyboard was recorded, thus allowing precise time-locking between the neural activity and the spectrotemporal content of the music imagery. This novel task design provided a unique opportunity to apply receptive field modeling techniques to quantitatively study neural encoding during auditory mental imagery. In both conditions, we built encoding models to predict high gamma neural activity (70-150 Hz) from the spectrogram representation of the recorded sound. We found robust spectrotemporal receptive fields during auditory imagery with substantial, but not complete overlap in frequency tuning and cortical location compared to receptive fields measured during auditory perception.

A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex

Birdsong is a complex vocal communication signal, and like humans, birds need to discriminate between similar sequences of sound with different meanings. The caudal mesopallium (CM) is a cortical-level auditory area implicated in song discrimination. CM neurons respond sparsely to conspecific song and are tolerant of production variability. Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics, which could contribute to the emergence of these higher-order functional properties. We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong. Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current. These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies.

Maintaining a stable mental representation of an object is an important task for sensory systems, requiring both recognizing the features required for identification and ignoring incidental changes in its presentation. The prevailing explanation for these processes emphasizes precise sets of connections between neurons that capture only the essential features of an object. However, the intrinsic dynamics of the neurons themselves, which determine how these inputs are transformed into spiking outputs, may also contribute to the neural computations underlying object recognition. To understand how intrinsic dynamics contribute to sensory coding, we constructed a computational model capable of simulating a neural response to an auditory stimulus using a detailed description of different intrinsic dynamics in a higher-order avian auditory area. The results of our simulation showed that intrinsic dynamics can have a profound effect on processes underlying object recognition. These findings challenge the view that patterns of connectivity alone account for the emergence of stable object representations and encourage greater consideration of the functional implications of the diversity of neurons in the brain.

Multidimensional stimulus encoding in the auditory nerve of the barn owl

Auditory perception depends on multi-dimensional information in acoustic signals that must be encoded by auditory nerve fibers (ANF). These dimensions are represented by filters with different frequency selectivities. Multiple models have been suggested; however, the identification of relevant filters and type of interactions has been elusive, limiting progress in modeling the cochlear output. Spike-triggered covariance analysis of barn owl ANF responses was used to determine the number of relevant stimulus filters and estimate the nonlinearity that produces responses from filter outputs. This confirmed that ANF responses depend on multiple filters. The first, most dominant filter was the spike-triggered average, which was excitatory for all neurons. The second and third filters could be either suppressive or excitatory with center frequencies above or below that of the first filter. The nonlinear function mapping the first two filter outputs to the spiking probability ranged from restricted to nearly circular-symmetric, reflecting different modes of interaction between stimulus dimensions across the sample. This shows that stimulus encoding in ANFs of the barn owl is multidimensional and exhibits diversity over the population, suggesting that models must allow for variable numbers of filters and types of interactions between filters to describe how sound is encoded in ANFs.

Challenging One Model With Many Stimuli: Simulating Responses in the Inferior Colliculus

Existing models to explain human psychophysics or neural responses are typically designed for a specific stimulus type and often fail for other stimuli. The ultimate goal for a neural model is to simulate responses to many stimuli, which may provide better insights into neural mechanisms. We tested the ability of modified same-frequency inhibition-excitation models for inferior colliculus neurons to simulate individual neuron responses to both amplitude-modulated sounds and tone-in-noise stimuli. Modifications to the model were guided by receptive fields computed with 2nd-order Wiener kernel analysis. This approach successfully simulated many individual neurons’ responses to different types of stimuli. Other neurons suggest limitations and future directions for modeling efforts.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation

Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinear and have generally been better characterized in terms of their decoding performance on prespecified tasks. Here we develop a biologically plausible decoding model for classification tasks, that we refer to as neural quadratic discriminant analysis (nQDA). Specifically, we reformulate an optimal quadratic classifier as an LN-LN computation, analogous to "subunit" encoding models that have been used to describe responses in retina and primary visual cortex. We propose a physiological mechanism by which the parameters of the nQDA classifier could be optimized, using a supervised variant of a Hebbian learning rule. As an example of its applicability, we show that nQDA provides a better account than many comparable alternatives for the transformation between neural representations in two high-level brain areas recorded as monkeys performed a visual delayed-match-to-sample task.

Stable encoding of sounds over a broad range of statistical parameters in the auditory cortex

Natural auditory scenes possess highly structured statistical regularities, which are dictated by the physics of sound production in nature, such as scale‐invariance. We recently identified that natural water sounds exhibit a particular type of scale invariance, in which the temporal modulation within spectral bands scales with the centre frequency of the band. Here, we tested how neurons in the mammalian primary auditory cortex encode sounds that exhibit this property, but differ in their statistical parameters. The stimuli varied in spectro‐temporal density and cyclo‐temporal statistics over several orders of magnitude, corresponding to a range of water‐like percepts, from pattering of rain to a slow stream. We recorded neuronal activity in the primary auditory cortex of awake rats presented with these stimuli. The responses of the majority of individual neurons were selective for a subset of stimuli with specific statistics. However, as a neuronal population, the responses were remarkably stable over large changes in stimulus statistics, exhibiting a similar range in firing rate, response strength, variability and information rate, and only minor variation in receptive field parameters. This pattern of neuronal responses suggests a potentially general principle for cortical encoding of complex acoustic scenes: while individual cortical neurons exhibit selectivity for specific statistical features, a neuronal population preserves a constant response structure across a broad range of statistical parameters.

A Framework for Speech Activity Detection Using Adaptive Auditory Receptive Fields

One of the hallmarks of sound processing in the brain is the ability of the nervous system to adapt to changing behavioral demands and surrounding soundscapes. It can dynamically shift sensory and cognitive resources to focus on relevant sounds. Neurophysiological studies indicate that this ability is supported by adaptively retuning the shapes of cortical spectro-temporal receptive fields (STRFs) to enhance features of target sounds while suppressing those of task-irrelevant distractors. Because an important component of human communication is the ability of a listener to dynamically track speech in noisy environments, the solution obtained by auditory neurophysiology implies a useful adaptation strategy for speech activity detection (SAD). SAD is an important first step in a number of automated speech processing systems, and performance is often reduced in highly noisy environments. In this paper, we describe how task-driven adaptation is induced in an ensemble of neurophysiological STRFs, and show how speech-adapted STRFs reorient themselves to enhance spectro-temporal modulations of speech while suppressing those associated with a variety of nonspeech sounds. We then show how an adapted ensemble of STRFs can better detect speech in unseen noisy environments compared to an unadapted ensemble and a noise-robust baseline. Finally, we use a stimulus reconstruction task to demonstrate how the adapted STRF ensemble better captures the spectrotemporal modulations of attended speech in clean and noisy conditions. Our results suggest that a biologically plausible adaptation framework can be applied to speech processing systems to dynamically adapt feature representations for improving noise robustness.

Dynamic representation of spectral edges in guinea pig primary auditory cortex

The central representation of a given acoustic motif is thought to be strongly context dependent, i.e., to rely on the spectrotemporal past and present of the acoustic mixture in which it is embedded. The present study investigated the cortical representation of spectral edges (i.e., where stimulus energy changes abruptly over frequency) and its dependence on stimulus duration and depth of the spectral contrast in guinea pig. We devised a stimulus ensemble composed of random tone pips with or without an attenuated frequency band (AFB) of variable depth. Additionally, the multitone ensemble with AFB was interleaved with periods of silence or with multitone ensembles without AFB. We have shown that the representation of the frequencies near but outside the AFB is greatly enhanced, whereas the representation of frequencies near and inside the AFB is strongly suppressed. These cortical changes depend on the depth of the AFB: although they are maximal for the largest depth of the AFB, they are also statistically significant for depths as small as 10 dB. Finally, the cortical changes are quick, occurring within a few seconds of stimulus ensemble presentation with AFB, and are very labile, disappearing within a few seconds after the presentation without AFB. Overall, this study demonstrates that the representation of spectral edges is dynamically enhanced in the auditory centers. These central changes may have important functional implications, particularly in noisy environments where they could contribute to preserving the central representation of spectral edges.

Decoding of auditory cortex signals with a LAMSTAR neural network

OBJECTIVES

Each neuron has a specific set of stimuli, which it preferentially responds to (the receptive field of the neuron). For implantable cortical prosthetic devices specific points of the cortex (or groups of neurons) have to be stimulated to create perceptions of sensory stimulus with specific attributes (such as frequency, temporal characteristics, etc). Such applications would need real time decoding of signals. Previously mathematical techniques, such as computing the receptive field (using electrophysiology data) and artificial neural networks (Kohonen network or SOM and back propagation network) have been used to decode neural signals.

METHODS

A Large Adaptive Memory Storage and Retrieval (LAMSTAR) neural-network-based decoder was designed to decode responses recorded from neurons in the auditory cortex. It was designed to identify the frequency of the tonal stimuli that elicited a particular discharge rate pattern recorded on two channels of a tungsten wire electrode array.

RESULTS

The network functioned efficiently as a decoder with 100% accuracy for the small sample of stimulus-response data used.

DISCUSSION

The results show that the network is effective in studying the functional organization of the auditory cortex and other sensory systems. Depending on the input sub-word, information about the kind of stimuli that activates particular parts of the sensory cortex can be studied.

The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments.

Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the "inputs" and "outputs", respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The "scaling-up" issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models.

Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli

The space of sensory stimuli is complex and high-dimensional. Yet, single neurons in sensory systems are typically affected by only a small subset of the vast space of all possible stimuli. A proper understanding of the input–output transformation represented by a given cell therefore requires the identification of the subset of stimuli that are relevant in shaping the neuronal response. As an extension to the commonly-used spike-triggered average, the analysis of the spike-triggered covariance matrix provides a systematic methodology to detect relevant stimuli. As originally designed, the consistency of this method is guaranteed only if stimuli are drawn from a Gaussian distribution. Here we present a geometric proof of consistency, which provides insight into the foundations of the method, in particular, into the crucial role played by the geometry of stimulus space and symmetries in the stimulus–response relation. This approach leads to a natural extension of the applicability of the spike-triggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription. Furthermore, we present a new resampling method for assessing statistical significance of identified relevant stimuli, applicable to spherical and elliptic stimulus distributions. Finally, we exemplify the modified method and compare it to other prescriptions given in the literature.

Improved similarity measures for small sets of spike trains

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in neuroprosthetic devices and electrophysiological experiments. Frequently only a few spike trains are available in each class. We derive analytical expressions for the small-sample bias present when comparing estimators of the time-dependent firing intensity. We then exploit analogies between the comparison of firing intensities and previously used spike train metrics and show that improved spike train measures can be successfully used for fitting neuron models to experimental data, for comparisons of spike trains, and classification of spike train data. In classification tasks, the improved similarity measures can increase the recovered information. We demonstrate that when similarity measures are used for fitting mathematical models, all previous methods systematically underestimate the noise. Finally, we show a striking implication of this deterministic bias by reevaluating the results of the single-neuron prediction challenge.

Understanding auditory spectro-temporal receptive fields and their changes with input statistics by efficient coding principles

Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed.

Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain

Birdsong is comprised of rich spectral and temporal organization, which might be used for vocal perception. To quantify how this structure could be used, we have reconstructed birdsong spectrograms by combining the spike trains of zebra finch auditory midbrain neurons with information about the correlations present in song. We calculated maximum a posteriori estimates of song spectrograms using a generalized linear model of neuronal responses and a series of prior distributions, each carrying different amounts of statistical information about zebra finch song. We found that spike trains from a population of mesencephalicus lateral dorsalis (MLd) neurons combined with an uncorrelated Gaussian prior can estimate the amplitude envelope of song spectrograms. The same set of responses can be combined with Gaussian priors that have correlations matched to those found across multiple zebra finch songs to yield song spectrograms similar to those presented to the animal. The fidelity of spectrogram reconstructions from MLd responses relies more heavily on prior knowledge of spectral correlations than temporal correlations. However, the best reconstructions combine MLd responses with both spectral and temporal correlations.

Context dependence of spectro-temporal receptive fields with implications for neural coding

The spectro-temporal receptive field (STRF) is frequently used to characterize the linear frequency-time filter properties of the auditory system up to the neuron recorded from. STRFs are extremely stimulus dependent, reflecting the strong non-linearities in the auditory system. Changes in the STRF with stimulus type (tonal, noise-like, vocalizations), sound level and spectro-temporal sound density are reviewed here. Effects on STRF shape of task and attention are also briefly reviewed. Models to account for these changes, potential improvements to STRF analysis, and implications for neural coding are discussed.

Representations of conspecific song by starling secondary forebrain auditory neurons: toward a hierarchical framework

The functional organization giving rise to stimulus selectivity in higher-order auditory neurons remains under active study. We explored the selectivity for motifs, spectrotemporally distinct perceptual units in starling song, recording the responses of 96 caudomedial mesopallium (CMM) neurons in European starlings (Sturnus vulgaris) under awake-restrained and urethane-anesthetized conditions. A subset of neurons was highly selective between motifs. Selectivity was correlated with low spontaneous firing rates and high spike timing precision, and all but one of the selective neurons had similar spike waveforms. Neurons were further tested with stimuli in which the notes comprising the motifs were manipulated. Responses to most of the isolated notes were similar in amplitude, duration, and temporal pattern to the responses elicited by those notes in the context of the motif. For these neurons, we could accurately predict the responses to motifs from the sum of the responses to notes. Some notes were suppressed by the motif context, such that removing other notes from motifs unmasked additional excitation. Models of linear summation of note responses consistently outperformed spectrotemporal receptive field models in predicting responses to song stimuli. Tests with randomized sequences of notes confirmed the predictive power of these models. Whole notes gave better predictions than did note fragments. Thus in CMM, auditory objects (motifs) can be represented by a linear combination of excitation and suppression elicited by the note components of the object. We hypothesize that the receptive fields arise from selective convergence by inputs responding to specific spectrotemporal features of starling notes.

Lagged cells in the inferior colliculus of the awake ferret

Neurons in the primary auditory cortex (AI) encode complex features of the spectral content of sound, such as direction selectivity. Recent findings of temporal symmetry in AI predict a specific organization of the subcortical input into the cortex that contributes to the emergence of direction selectivity. We demonstrate two subpopulations of neurons in the central nucleus of the inferior colliculus, which differ in their steady-state temporal response profile: lagged and non-lagged. The lagged cells (23%) are shifted in temporal phase with respect to non-lagged cells, and are characterized by an 'inhibition first' and delayed excitation in their spectro-temporal receptive fields. Non-lagged cells (77%) have a canonical 'excitation first' response. However, we find no difference in the response onset latency to pure tone stimuli between the two subpopulations. Given the homogeneity of tonal response latency, we predict that these lagged cells receive inhibitory input mediated by cortical feedback projections.

Increasing spectrotemporal sound density reveals an octave-based organization in cat primary auditory cortex

Auditory neurons are likely adapted to process complex stimuli, such as vocalizations, which contain spectrotemporal modulations. However, basic properties of auditory neurons are often derived from tone pips presented in isolation, which lack spectrotemporal modulations. In this context, it is unclear how to deduce the functional role of auditory neurons from their tone pip-derived tuning properties. In this study, spectrotemporal receptive fields (STRFs) were obtained from responses to multi-tone stimulus ensembles differing in their average spectrotemporal density (i.e., number of tone pips per second). STRFs for different stimulus densities were derived from multiple single-unit activity (MUA) and local field potentials (LFPs), simultaneously recorded in primary auditory cortex of cats. Consistent with earlier studies, we found that the spectral bandwidth was narrower for MUA compared with LFPs. Both neural firing rate and LFP amplitude were reduced when the density of the stimulus ensemble increased. Surprisingly, we found that increasing the spectrotemporal sound density revealed with increasing clarity an over-representation of response peaks at frequencies of approximately 3, 5, 10, and 20 kHz, in both MUA- and LFP-derived STRFs. Although the decrease in spectral bandwidth and neural activity with increasing stimulus density can likely be accounted for by forward suppression, the mechanisms underlying the over-representation of the octave-spaced response peaks are unclear. Plausibly, the over-representation may be a functional correlate of the periodic pattern of corticocortical connections observed along the tonotopic axis of cat auditory cortex.

The role of broadband inhibition in the rate representation of spectral cues for sound localization in the inferior colliculus

Previous investigations have shown that a subset of inferior colliculus neurons, which have been designated type O units, respond selectively to isolated features of the cat's head-related transfer functions (HRTFs: the directional transformation of a free-field sound as it propagates from the head to the eardrum). Based on those results, it was hypothesized that type O units would show enhanced spatial tuning in a virtual sound field that conveyed the full complement of HRTF-based localization cues. As anticipated, a number of neurons produced representations of virtual sound source locations that were spatially tuned, level tolerant, and effective under monaural conditions. Preferred locations were associated with spectral cues that complemented the highly individualized broadband inhibitory patterns of tuned neurons. That is, higher response magnitudes were achieved when spectral peaks coincided with excitatory influences at best frequency (BF: the most sensitive frequency) and spectral notches fell within flanking inhibitory regions. The directionally dependent modulation of narrowband ON-BF excitation by broadband OFF-BF inhibition was not a unique property of type O units.

The consequences of response nonlinearities for interpretation of spectrotemporal receptive fields

Neurons in the central auditory system are often described by the spectrotemporal receptive field (STRF), conventionally defined as the best linear fit between the spectrogram of a sound and the spike rate it evokes. An STRF is often assumed to provide an estimate of the receptive field of a neuron, i.e., the spectral and temporal range of stimuli that affect the response. However, when the true stimulus-response function is nonlinear, the STRF will be stimulus dependent, and changes in the stimulus properties can alter estimates of the sign and spectrotemporal extent of receptive field components. We demonstrate analytically and in simulations that, even when uncorrelated stimuli are used, interactions between simple neuronal nonlinearities and higher-order structure in the stimulus can produce STRFs that show contributions from time-frequency combinations to which the neuron is actually insensitive. Only when spectrotemporally independent stimuli are used does the STRF reliably indicate features of the underlying receptive field, and even then it provides only a conservative estimate. One consequence of these observations, illustrated using natural stimuli, is that a stimulus-induced change in an STRF could arise from a consistent but nonlinear neuronal response to stimulus ensembles with differing higher-order dependencies. Thus, although the responses of higher auditory neurons may well involve adaptation to the statistics of different stimulus ensembles, stimulus dependence of STRFs alone, or indeed of any overly constrained stimulus-response mapping, cannot demonstrate the nature or magnitude of such effects.

Receptive field for dorsal cochlear nucleus neurons at multiple sound levels

Neurons in the dorsal cochlear nucleus (DCN) exhibit nonlinearities in spectral processing, which make it difficult to predict the neurons' responses to stimuli. Here, we consider two possible sources of nonlinearity: nonmonotonic responses as sound level increases due to inhibition and interactions between frequency components. A spectral weighting function model of rate responses is used; the model approximates the neuron's rate response as a weighted sum of the frequency components of the stimulus plus a second-order sum that captures interactions between frequencies. Such models approximate DCN neurons well at low spectral contrast, i.e., when the SD (contrast) of the stimulus spectrum is limited to 3 dB. This model is compared with a first-order sum with weights that are explicit functions of sound level, so that the low-contrast model is extended to spectral contrasts of 12 dB, the range of natural stimuli. The sound-level-dependent weights improve prediction performance at large spectral contrast. However, the interactions between frequencies, represented as second-order terms, are more important at low spectral contrast. The level-dependent model is shown to predict previously described patterns of responses to spectral edges, showing that small changes in the inhibitory components of the receptive field can produce large changes in the responses of the neuron to features of natural stimuli. These results provide an effective way of characterizing nonlinear auditory neurons incorporating stimulus-dependent sensitivity changes. Such models could be used for neurons in other sensory systems that show similar effects.

Responses of inferior collicular cells to species-specific vocalizations in normal and enucleated rats

The inferior colliculus (IC) is an obligatory relay for the ascending and descending auditory pathways. Cells in this brainstem structure not only analyze auditory stimuli but they also play a major role in multi-modal integration of auditory and visual information. The aim of the present study was to determine whether cells in the central nucleus of the inferior colliculus (CNIC) of normal rats respond selectively to complex auditory signals, such as species-specific vocalizations, and compare their responses to those obtained in neonatal bilateral enucleated (P2-P3) adult rats. Extra-cellular recordings were carried out in anesthetized normal and enucleated rats using auditory stimuli (pure tones, broadband noise and vocalizations) presented in free field in a semi-anechoic chamber. The results indicate that most cells in the CNIC of both groups respond selectively to species-specific vocalizations better than to the same but inverted sounds. No significant differences were found between the normal and enucleated rat groups in their responses to broadband noise and pure tones.

Acoustic Features of Rhesus Vocalizations and Their Representation in the Ventrolateral Prefrontal Cortex

Communication is one of the fundamental components of both human and nonhuman animal behavior. Auditory communication signals (i.e., vocalizations) are especially important in the socioecology of several species of nonhuman primates such as rhesus monkeys. In rhesus, the ventrolateral prefrontal cortex (vPFC) is thought to be part of a circuit involved in representing vocalizations and other auditory objects. To further our understanding of the role of the vPFC in processing vocalizations, we characterized the spectrotemporal features of rhesus vocalizations, compared these features with other classes of natural stimuli, and then related the rhesus-vocalization acoustic features to neural activity. We found that the range of these spectrotemporal features was similar to that found in other ensembles of natural stimuli, including human speech, and identified the subspace of these features that would be particularly informative to discriminate between different vocalizations. In a first neural study, however, we found that the tuning properties of vPFC neurons did not emphasize these particularly informative spectrotemporal features. In a second neural study, we found that a first-order linear model (the spectrotemporal receptive field) is not a good predictor of vPFC activity. The results of these two neural studies are consistent with the hypothesis that the vPFC is not involved in coding the first-order acoustic properties of a stimulus but is involved in processing the higher-order information needed to form representations of auditory objects.

Tuning properties of turtle auditory nerve fibers: evidence for suppression and adaptation

Second-order reverse correlation (second-order Wiener-kernel analysis) was carried out between spike responses in single afferent units from the basilar papilla of the red-eared turtle and band limited white noise auditory stimuli. For units with best excitatory frequencies (BEFs) below approximately 500 Hz, the analysis revealed suppression similar to that observed previously in anuran amphibians. For units with higher BEFs, the analysis revealed dc response with narrow-band tuning centered about the BEF, combined with broad-band ac response at lower frequencies. For all units, the analysis revealed the relative timing and tuning of excitation and various forms of inhibitory or suppressive effects.

Complete functional characterization of sensory neurons by system identification

System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.

New variation on the derivation of spectro-temporal receptive fields for primary auditory afferent axons

The spectro-temporal receptive field [Hear. Res 5 (1981) 147; IEEE Trans BME 15 (1993) 177] provides an explicit image of the spectral and temporal aspects of the responsiveness of a primary auditory afferent axon. It exhibits the net effects of the competition between excitatory and inhibitory (or suppressive) phenomena. In this paper, we introduce a method for derivation of the spectro-temporal receptive field directly from a second-order Wiener kernel (produced by second-order reverse correlation between spike responses and broad-band white-noise stimulus); and we expand the concept of the spectro-temporal receptive field by applying the new method not only to the second-order kernel itself, but also to its excitatory and inhibitory subkernels. This produces separate spectro-temporal images of the excitatory and inhibitory phenomena putatively underlying the competition. Applied, in simulations, to models with known underlying excitatory and suppressive tuning and timing properties, the method successfully extracted a faithful image of those properties for excitation and one for inhibition. Applied to three auditory axons from the frog, it produced images consistent with previously published physiology.

Methods for the analysis of auditory processing in the brain

Understanding song perception and singing behavior in birds requires the study of auditory processing of complex sounds throughout the avian brain. We can divide the basics of auditory perception into two general processes: (1) encoding, the process whereby sound is transformed into neural activity and (2) decoding, the process whereby patterns of neural activity take on perceptual meaning and therefore guide behavioral responses to sounds. In birdsong research, most studies have focused on the decoding process: What are the responses of the specialized auditory neurons in the song control system? and What do they mean for the bird? Recently, new techniques addressing both encoding and decoding have been developed for use in songbirds. Here, we first describe some powerful methods for analyzing what acoustical aspects of complex sounds like songs are encoded by auditory processing neurons in songbird brain. These methods include the estimation and analysis of spectro-temporal receptive fields (STRFs) for auditory neurons. Then we discuss the decoding methods that have been used to understand how songbird neurons may discriminate among different songs and other sounds based on mean spike-count rates.

New variations on the derivation of spectro-temporal receptive fields for primary auditory afferent axons

The spectro-temporal receptive field [Hear. Res 5 (1981) 147; IEEE Trans BME 15 (1993) 177] provides an explicit image of the spectral and temporal aspects of the responsiveness of a primary auditory afferent axon. It exhibits the net effects of the competition between excitatory and inhibitory (or suppressive) phenomena. In this paper, we introduce a method for derivation of the spectro-temporal receptive field directly from a second-order Wiener kernel (produced by second-order reverse correlation between spike responses and broad-band white-noise stimulus); and we expand the concept of the spectro-temporal receptive field by applying the new method not only to the second-order kernel itself, but also to its excitatory and inhibitory subkernels. This produces separate spectro-temporal images of the excitatory and inhibitory phenomena putatively underlying the competition. Applied, in simulations, to models with known underlying excitatory and suppressive tuning and timing properties, the method successfully extracted a faithful image of those properties for excitation and one for inhibition. Applied to three auditory axons from the frog, it produced images consistent with previously published physiology.

Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex

The mouse is a promising model system for auditory cortex research because of the powerful genetic tools available for manipulating its neural circuitry. Previous studies have identified two tonotopic auditory areas in the mouse-primary auditory cortex (AI) and anterior auditory field (AAF)- but auditory receptive fields in these areas have not yet been described. To establish a foundation for investigating auditory cortical circuitry and plasticity in the mouse, we characterized receptive-field structure in AI and AAF of anesthetized mice using spectrally complex and temporally dynamic stimuli as well as simple tonal stimuli. Spectrotemporal receptive fields (STRFs) were derived from extracellularly recorded responses to complex stimuli, and frequency-intensity tuning curves were constructed from responses to simple tonal stimuli. Both analyses revealed temporal differences between AI and AAF responses: peak latencies and receptive-field durations for STRFs and first-spike latencies for responses to tone bursts were significantly longer in AI than in AAF. Spectral properties of AI and AAF receptive fields were more similar, although STRF bandwidths were slightly broader in AI than in AAF. Finally, in both AI and AAF, a substantial minority of STRFs were spectrotemporally inseparable. The spectrotemporal interaction typically appeared in the form of clearly disjoint excitatory and inhibitory subfields or an obvious spectrotemporal slant in the STRF. These data provide the first detailed description of auditory receptive fields in the mouse and suggest that although neurons in areas AI and AAF share many response characteristics, area AAF may be specialized for faster temporal processing.

Frequency-specific interaural level difference tuning predicts spatial response patterns of space-specific neurons in the barn owl inferior colliculus

Space-specific neurons in the barn owl's inferior colliculus have spatial receptive fields (RFs) because of sensitivity to interaural time difference and frequency-specific interaural level difference (ILD). These neurons are assumed to be tuned to the frequency-specific ILDs occurring at their spatial RFs, but attempts to assess this tuning with traditional narrowband stimuli have had limited success. Indeed, tuning assessed in this manner, when processed via a linear model of spectral integration, typically explains only approximately half the variance in spatial response patterns. Here we report our findings that frequency-specific ILD tuning of space-specific neurons, when assessed from responses to broadband stimuli, predicted nearly 75% of the variance in spatial responses, using a linear model of spectral integration (p < 0.0001; n = 97 neurons). Furthermore, when we tested neurons using only those frequencies we found to be spatially relevant, we saw that their responses were similar to those elicited by broadband stimuli. When we used frequencies not identified as spatially relevant, such similarity was lacking. Furthermore, spectral components that elicited high firing rates when presented as narrowband stimuli were found in several cases to be irrelevant for or detrimental to the definition of spatial RFs. Thus, neurons achieved sharp spatial tuning by selecting for ILDs of a subset of spectral components in noise, some of which were not identified using narrowband stimuli.

Tuning and timing in the gerbil ear: Wiener-kernel analysis

Information about the tuning and timing of excitation in cochlear axons with low-characteristic frequency (CF) is embodied in the first-order Wiener kernel, or reverse correlation function. For high-CF axons, the highest-ranking eigenvector (or singular vector) of the second-order Wiener kernel often can serve as a surrogate for the first-order kernel, providing the same information. For mid-CF axons, the two functions are essentially identical. In this paper we apply these tools to gerbil cochlear-nerve axons with CFs ranging from 700 Hz to 14 kHz. Eigen or singular-value decomposition of the second-order Wiener kernel allows us to separate excitatory and suppressive effects, and to determine precisely the timing of the latter.

Summation of spatiotemporal input patterns in leaky integrate-and-fire neurons: application to neurons in the cochlear nucleus receiving converging auditory nerve fiber input

The response of leaky integrate-and-fire neurons is analyzed for periodic inputs whose phases vary with their spatial location. The model gives the relationship between the spatial summation distance and the degree of phase locking of the output spikes (i.e., locking to the periodic stochastic inputs, measured by the synchronization index). The synaptic inputs are modeled as an inhomogeneous Poisson process, and the analysis is carried out in the Gaussian approximation. The model has been applied to globular bushy cells of the cochlear nucleus, which receive converging inputs from auditory nerve fibers that originate at neighboring sites in the cochlea. The model elucidates the roles played by spatial summation and coincidence detection, showing how synchronization decreases with an increase in both frequency and spatial spread of inputs. It also shows under what conditions an enhancement of synchronization of the output relative to the input takes place.

Feature analysis of natural sounds in the songbird auditory forebrain

Although understanding the processing of natural sounds is an important goal in auditory neuroscience, relatively little is known about the neural coding of these sounds. Recently we demonstrated that the spectral temporal receptive field (STRF), a description of the stimulus-response function of auditory neurons, could be derived from responses to arbitrary ensembles of complex sounds including vocalizations. In this study, we use this method to investigate the auditory processing of natural sounds in the birdsong system. We obtain neural responses from several regions of the songbird auditory forebrain to a large ensemble of bird songs and use these data to calculate the STRFs, which are the best linear model of the spectral-temporal features of sound to which auditory neurons respond. We find that these neurons respond to a wide variety of features in songs ranging from simple tonal components to more complex spectral-temporal structures such as frequency sweeps and multi-peaked frequency stacks. We quantify spectral and temporal characteristics of these features by extracting several parameters from the STRFs. Moreover, we assess the linearity versus nonlinearity of encoding by quantifying the quality of the predictions of the neural responses to songs obtained using the STRFs. Our results reveal successively complex functional stages of song analysis by neurons in the auditory forebrain. When we map the properties of auditory forebrain neurons, as characterized by the STRF parameters, onto conventional anatomical subdivisions of the auditory forebrain, we find that although some properties are shared across different subregions, the distribution of several parameters is suggestive of hierarchical processing.

Representation of temporal features of complex sounds by the discharge patterns of neurons in the owl's inferior colliculus

The spiking pattern evoked in cells of the owl's inferior colliculus by repeated presentation of the same broadband noise was found to be highly reproducible and synchronized with the temporal features of the noise stimulus. The pattern remained largely unchanged when the stimulus was presented from spatial loci that evoke similar average firing rates. To better understand this patterning, we computed the pre-event stimulus ensemble (PESE)-the average of the stimuli that preceded each spike. Computing the PESE by averaging the pressure waveforms produced a noisy, featureless trace, suggesting that the patterning was not synchronized to a particular waveform in the fine structure. By contrast, computing the PESE by averaging the stimulus envelope revealed an average envelope waveform, the "PESE envelope," typically having a peak preceded by a trough. Increasing the overall stimulus level produced PESE envelopes with higher amplitudes, suggesting a decrease in the jitter of the cell's response. The effect of carrier frequency on the PESE envelope was investigated by obtaining a cell's response to broadband noise and either estimating the PESE envelope for each spectral band or by computing a spectrogram of the stimulus prior to each spike. Either method yielded the cell's PESE spectrogram, a plot of the average amplitude of each carrier-frequency component at various pre-spike times. PESE spectrograms revealed surfaces with peaks and troughs at certain frequencies and pre-spike times. These features are collectively called the spectrotemporal receptive field (STRF). The shape of the STRF showed that in many cases, the carrier frequency can affect the PESE envelope. The modulation transfer function (MTF), which describes a cell's ability to respond to time-varying amplitudes, was estimated with sinusoidally amplitude-modulated (SAM) noises. Comparison of the PESE envelope with the MTF in the time and frequency domains showed that the two were closely matched, suggesting that a cell's response to SAM stimuli is largely predictable from its response to a noise-modulated carrier. The STRF is considered to be a model of the linear component of a system's response to dynamic stimuli. Using the STRF, we estimated the degree to which we could predict a cell's response to an arbitrary broadband noise by comparing the convolution of the STRF and the envelope of the noise with the cell's post-stimulus time histogram to the same noise. The STRF explained 18-46% of the variance of a cell's response to broadband noise.

Neural representation and the cortical code

The principle function of the central nervous system is to represent and transform information and thereby mediate appropriate decisions and behaviors. The cerebral cortex is one of the primary seats of the internal representations maintained and used in perception, memory, decision making, motor control, and subjective experience, but the basic coding scheme by which this information is carried and transformed by neurons is not yet fully understood. This article defines and reviews how information is represented in the firing rates and temporal patterns of populations of cortical neurons, with a particular emphasis on how this information mediates behavior and experience.

Robust spectrotemporal reverse correlation for the auditory system: optimizing stimulus design

The spectrotemporal receptive field (STRF) is a functional descriptor of the linear processing of time-varying acoustic spectra by the auditory system. By cross-correlating sustained neuronal activity with the dynamic spectrum of a spectrotemporally rich stimulus ensemble, one obtains an estimate of the STRF. In this article, the relationship between the spectrotemporal structure of any given stimulus and the quality of the STRF estimate is explored and exploited. Invoking the Fourier theorem, arbitrary dynamic spectra are described as sums of basic sinusoidal components--that is, moving ripples. Accurate estimation is found to be especially reliant on the prominence of components whose spectral and temporal characteristics are of relevance to the auditory locus under study and is sensitive to the phase relationships between components with identical temporal signatures. These and other observations have guided the development and use of stimuli with deterministic dynamic spectra composed of the superposition of many temporally orthogonal moving ripples having a restricted, relevant range of spectral scales and temporal rates. The method, termed sum-of-ripples, is similar in spirit to the white-noise approach but enjoys the same practical advantages--which equate to faster and more accurate estimation--attributable to the time-domain sum-of-sinusoids method previously employed in vision research. Application of the method is exemplified with both modeled data and experimental data from ferret primary auditory cortex (AI).

Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds

The stimulus-response function of many visual and auditory neurons has been described by a spatial-temporal receptive field (STRF), a linear model that for mathematical reasons has until recently been estimated with the reverse correlation method, using simple stimulus ensembles such as white noise. Such stimuli, however, often do not effectively activate high-level sensory neurons, which may be optimized to analyze natural sounds and images. We show that it is possible to overcome the simple-stimulus limitation and then use this approach to calculate the STRFs of avian auditory forebrain neurons from an ensemble of birdsongs. We find that in many cases the STRFs derived using natural sounds are strikingly different from the STRFs that we obtained using an ensemble of random tone pips. When we compare these two models by assessing their predictions of neural response to the actual data, we find that the STRFs obtained from natural sounds are superior. Our results show that the STRF model is an incomplete description of response properties of nonlinear auditory neurons, but that linear receptive fields are still useful models for understanding higher level sensory processing, as long as the STRFs are estimated from the responses to relevant complex stimuli.

Population responses to multifrequency sounds in the cat auditory cortex: four-tone complexes

Population responses to two-tone and four-tone sounds were recorded in primary auditory cortex of anesthetized cats. The stimuli were delivered through a sealed, calibrated sound delivery system. The envelope of the neural signal (short time mean absolute value, MABS) was recorded extracellularly from six microelectrodes simultaneously. A new method was developed to describe the responses to the four-tone complexes. The responses were represented as sums of contributions of different orders. The first order contributions described the effect of the single frequencies appearing in the stimulus. The second order contributions described the modulatory effect of the pairs of frequencies. Higher order contributions could in principle be computed. This paper concentrates on the mean onset responses. The extent to which the first and second order contributions described the onset responses was assessed in two ways. First, the actual responses to two-tone stimuli were compared with those predicted using the contributions computed from the four-tone stimuli. Second, the residual variance in the responses, after the subtraction of the first and second order contributions, was computed and compared with the variability in the responses to repetitions of the same stimulus. The first type of analysis showed good quantitative agreement between the predicted and the measured two-tone responses. The second type of analysis showed that the first and second order contributions were often sufficient to predict the responses to four-tone stimuli up to the level of the variability in the responses to repetitions of a single stimulus. In conjunction with the results of the companion paper (Nelken et al., 1994a) it is concluded that the onset responses to multifrequency sounds are shaped mainly by the single frequency content of the sound and by two-tone interactions, and that higher order interactions contribute much less to the responses. It follows that single-tone effects and two-tone interactions are necessary and sufficient to explain the mean population onset responses to the four-tone stimuli. More information can be coded in the temporal evolution of the responses.

Wiener and Volterra analyses applied to the auditory system

The application of a particular branch of non-linear system analysis, the functional series expansion or integral method, to the auditory system is reviewed. Both the Volterra and Wiener approach are discussed and an extension of the Wiener method from its traditional white-noise stimulus approach to that of Poisson distributed clicks is presented. This type of analysis has been applied to compound and single-unit responses from the auditory nerve, cochlear nucleus, auditory midbrain and medial geniculate body. Most studies have estimated only first-order Wiener kernels but in recent years second-order Wiener and Volterra kernels have been estimated, particularly with reference to dynamic non-linearities. A particular form of second-order analysis, the Spectro Temporal Receptive Field, offers an alternative to first-order cross-correlation when phase-lock is absent. The correlation method has revealed that neural synchronization is less affected by intensity changes and damage to the hair cells than is neural firing rate. Although the presence of the static cochlear non-linearity could be demonstrated on the basis of the intensity dependence of the first-order Wiener kernel, the identification of the exact form of the nonlinearity of the peripheral auditory system on basis of higher-order Wiener kernels has so far been inconclusive. However, successes of the method can be found in the description of the dynamic non-linearities and non-linear neural interactions.

Setting complex tasks to single units in the avian auditory forebrain. II. Do we really need natural stimuli to describe neuronal response characteristics?

The response characteristic of auditory forebrain neurons in the European starling was established both with artificial stimuli (AS) and a conspecific territorial song as a natural stimulus (NS1). Applying experimenter-centred statistical methods for response detection and for scaling response strength, and spike-triggered analyses for the delimitation of the key sound parameters (spectrotemporal receptive field STRF, Aertsen et al. 1980) the study aimed at disclosing differences in the processing of the two stimulus classes, AS and NS. With the STRF as reference, we find congruence (1) in the best frequency with those determined under sweep and bandpass noise stimulation, (2) in response latency, and (3) in response-intensity dependence, further similarity in the overall frequency characteristic. Partitioning the song into 42 acoustically defined segments allowed to further delimit the response criteria under natural stimulation. They are easily understood from the AS response characteristics: (1) In the neuronal sample as a whole, long segments are more effective than short and, among the short, loud segments are more effective than faint; (2) Units showing their best excitatory response to AS in a certain frequency band are most probably excited by segments with a high proportion of their power concentrated upon or near this band; (3) Units with a slow (build-up) AS response react to a lower number of song segments than those dynamically following AS transients. Our data give no hint towards adaptive, feature detection properties of single neurons in field L. Instead, these neurons appear to base their response solely on the short-time spectrotemporal structure of the stimulus, irrespective of its natural or artificial origin.

Single-unit characteristics in the auditory midbrain of the immobilized grassfrog

The anuran auditory midbrain of the grassfrog (Rana temporaria L.) was studied by a combined spectro-temporal analysis of sound preceding neural events. From the spectro-temporal sensitivities (STS) estimates of best frequencies (BF) and latencies (LT) were derived. Several types of STSs were observed: monomodal excitatory STSs comprised about half of the cases. Bimodal excitatory STSs, i.e. STSs with two discrete excitation regions, were observed in about 25%. Trimodal and broadly tuned STSs comprised about 5%. The remaining 20% of the STSs were characterized by inhibitory phenomena such as pure inhibition, sideband inhibition and post-activation inhibition. The distribution of best frequencies matches the frequency spectrum of the animal's vocalizations. A relative absence of monomodal units was noted in the mid frequency range. The distribution of latencies was bimodal over the range 7-108 ms. For each unit 6 functional parameters were determined; besides BF and LT these were: form of the STS (i.e. monomodality versus multimodality), spontaneous activity, binaural interaction, and firing mode (i.e. sustained versus transient) upon continuous noises stimulation. In addition, two structural parameters were considered: location in the torus and action potential waveform. Large correlations appeared between LT and action potential waveform, and between BF and binaural interaction type. Tonotopy was not found. A comparison was made between results from this study with a previous study on lightly anesthetized grassfrogs, using the same stimulus paradigms (D.J. Hermes et al. (1981): Hearing Res. 5, 147-178; D.J. Hermes et al. (1982): Hearing Res. 6, 103-126). Spontaneous activity, inhibitory phenomena and complex STSs were common using immobilization, whereas these have hardly been observed using anesthesia. Furthermore, interdependencies between the neural characteristics are substantially weaker for the immobilized preparation.

Reverse-correlation methods in auditory research

Single unit recordings have provided us with a basis for understanding the auditory system, especially about how it behaves under stimulation with simple sounds such as clicks and tones. The experimental as well as the theoretical approach to single unit studies has been dichotomous. One approach, the more familiar, gives a representation of nervous system activity in the form of peri-stimulus-time (PST) histograms, period histograms, iso-intensity rate curves and frequency tuning curves. This approach observes the neural output of units in the various nuclei in the auditory nervous system, and, faced with the random way in which the neurons respond to sound, proceeds by repeatedly presenting the same stimulus in order to obtain averaged results. These are the various histogram procedures (Gerstein & Kiang, 1960; Kiang et al. 1965).

Prediction of the responses of auditory neurons in the midbrain of the grass frog based on the spectro-temporal receptive field

The spectro-temporal receptive field (STRF) of an auditory neuron represents those characteristics of the sound stimulus in both the time and frequency domain that affect the firing probability of the neuron. The STRF is determined under stationary stimulus conditions for Gaussian wide-band noise. It has been demonstrated that for some neurons the response to that noise could to a considerable extent be derived from the STRF. In the present study the usefulness of the STRF is tested to predict responses to other stimuli such as noise with different frequency content and to species-specific vocalisations. It appears that the predicted response to vocalisations is at best in qualitative agreement with the actual response.