A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.     

The Essential Complexity of Auditory Receptive Fields

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.     

A new method for adjusting neural response jitter in the STRF obtained by spike-trigger averaging

Sensitivity of central auditory neurons to frequency modulated (FM) sound is often characterized based on spectro-temporal receptive field (STRF), which is generated by spike-trigger averaging a random stimulus. Due to the inherent property of time variability in neural response, this method erroneously represents the response jitter as stimulus jitter in the STRF. To reveal the trigger features more clearly, we have implemented a method that minimizes this error. Neural spikes from the brainstem of urethane-anesthetized rats were first recorded in response to two sets of FM stimuli: (a) a random FM tone for the generation of STRF and (b) a family of linear FM ramps for the determination of FM 'trigger point'. Based on the first dataset, STRFs were generated using spike-trigger averaging. Individual modulating waveforms were then matched with respect to their mean waveform at time-windows of a systematically varied length. A stable or optimal variance time profile was found at a particular window length. At this optimal window length, we performed delay adjustments. A marked sharpening in the FM bands in the STRF was found. Results were consistent with the FM 'trigger point' as estimated by the linear FM ramps. We concluded that the present approach of adjusting response jitter was effective in delineating FM trigger features in the STRF.     

Temporal processing and adaptation in the songbird auditory forebrain

Songbird auditory neurons must encode the dynamics of natural sounds at many volumes. We investigated how neural coding depends on the distribution of stimulus intensities. Using reverse-correlation, we modeled responses to amplitude-modulated sounds as the output of a linear filter and a nonlinear gain function, then asked how filters and nonlinearities depend on the stimulus mean and variance. Filter shape depended strongly on mean amplitude (volume): at low mean, most neurons integrated sound over many milliseconds, while at high mean, neurons responded more to local changes in amplitude. Increasing the variance (contrast) of amplitude modulations had less effect on filter shape but decreased the gain of firing in most cells. Both filter and gain changes occurred rapidly after a change in statistics, suggesting that they represent nonlinearities in processing. These changes may permit neurons to signal effectively over a wider dynamic range and are reminiscent of findings in other sensory systems.     

Contrast gain control in auditory cortex

The auditory system must represent sounds with a wide range of statistical properties. One important property is the spectrotemporal contrast in the acoustic environment: the variation in sound pressure in each frequency band, relative to the mean pressure. We show that neurons in ferret auditory cortex rescale their gain to partially compensate for the spectrotemporal contrast of recent stimulation. When contrast is low, neurons increase their gain, becoming more sensitive to small changes in the stimulus, although the effectiveness of contrast gain control is reduced at low mean levels. Gain is primarily determined by contrast near each neuron's preferred frequency, but there is also a contribution from contrast in more distant frequency bands. Neural responses are modulated by contrast over timescales of ～100?ms. By using contrast gain control to expand or compress the representation of its inputs, the auditory system may be seeking an efficient coding of natural sounds.     

The linearity of emergent spectro-temporal receptive fields in a model of auditory cortex

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.     

Frequency and space representation in the primary auditory cortex of the frequency modulating batEptesicus fuscus

1. Frequency and space representation in the auditory cortex of the big brown bat, Eptesicus fuscus, were studied by recording responses of 223 neurons to acoustic stimuli presented in the bat's frontal auditory space. 2. The majority of the auditory cortical neurons were recorded at a depth of less than 500 microns with a response latency between 8 and 20 ms. They generally discharged phasically and had nonmonotonic intensity-rate functions. The minimum threshold, (MT) of these neurons was between 8 and 82 dB sound pressure level (SPL). Half of the cortical neurons showed spontaneous activity. All 55 threshold curves are V-shaped and can be described as broad, intermediate, or narrow. 3. Auditory cortical neurons are tonotopically organized along the anteroposterior axis of the auditory cortex. High-frequency-sensitive neurons are located anteriorly and low-frequency-sensitive neurons posteriorly. An overwhelming majority of neurons were sensitive to a frequency range between 30 and 75 kHz. 4. When a sound was delivered from the response center of a neuron on the bat's frontal auditory space, the neuron had its lowest MT. When the stimulus amplitude was increased above the MT, the neuron responded to sound delivered within a defined spatial area. The response center was not always at the geometric center of the spatial response area. The latter also expanded with stimulus amplitude. High-frequency-sensitive neurons tended to have smaller spatial response areas than low-frequency-sensitive neurons. 5. Response centers of all 223 neurons were located between 0 degrees and 50 degrees in azimuth, 2 degrees up and 25 degrees down in elevation of the contralateral frontal auditory space. Response centers of auditory cortical neurons tended to move toward the midline and slightly downward with increasing best frequency. 6. Auditory space representation appears to be systematically arranged according to the tonotopic axis of the auditory cortex. Thus, the lateral space is represented posteriorly and the middle space anteriorly. Space representation, however, is less systematic in the vertical direction. 7. Auditory cortical neurons are columnarly organized. Thus, the BFs, MTs, threshold curves, azimuthal location of response centers, and auditory spatial response areas of neurons sequentially isolated from an orthogonal electrode penetration are similar.     

The Spectro-Temporal Receptive Field

The Spectro-Temporal Receptive Field (STRF) of an auditory neuron has been introduced experimentally on the base of the average spectrotemporal structure of the acoustic stimuli which precede the occurrence of action potentials (Aertsen et al., 1980, 1981). In the present paper the STRF is considered in the general framework of nonlinear system theory, especially in the form of the Volterra integral representation. The STRF is proposed to be formally identified with a linear functional of the second order Volterra kernel. The experimental determination of the STRF leads to a formulation in terms of the Wiener expansion where the kernels can be identified by evaluation of the system's input-output correlations. For a Gaussian stimulus ensemble and a nonlinear system with no even order contributions of order higher than two, it is shown that the second order cross correlation of stimulus and response, normalized with respect to the spectral contents of the stimulus ensemble, leads to the stimulus-invariant spectrotemporal receptive field. The investigation of stimulus-invariance of the STRF for more general nonlinear systems and for stimulus ensembles which can be generated by nonlinear transformations of Gaussian noise involve the evaluation of higher order stimulus-response correlation functions.     

不同的声音强度对大鼠听皮层神经元特征频率可塑性的影响

目的：探讨声音强度对大鼠听皮层神经元特征频率可塑性的影响。方法：采用常规电生理学细胞外记录技术,测定不同声刺激强度下,听皮层神经元的特征频率和调谐曲线,比较条件刺激前后的变化。结果：在条件刺激声频率和神经元的特征频率相差±1.0kHz范围内,条件刺激诱导的神经元特征频率可塑性与条件刺激强度有关,较高的刺激强度比较低刺激强度诱导的特征频率可塑性概率高;特征频率可塑性的概率与神经元的频率调谐曲线类型相关,但这种相关几乎不受条件刺激声强度影响。结论：条件声刺激强度可明显影响大鼠听皮层神经元特征频率的可塑性。     

Latency represents sound frequency in mouse IC

Frequency is one of the fundamental parameters of sound.The frequency of an acoustic stimulus can be represented by a neural response such as spike rate,and/or first spike latency(FSL)of a given neuron.The spike rates/frequency function of most neurons changes with different acoustic ampli-tudes,whereas FSL/frequency function is highly stable.This implies that FSL might represent the fre-quency of a sound stimulus more efficiently than spike rate.This study involved representations of acoustic frequency by spike rate and FSL of central inferior colliculus(IC)neurons responding to free-field pure-tone stimuli.We found that the FSLs of neurons responding to characteristic frequency(CF)of sound stimulus were usually the shortest,regardless of sound intensity,and that spike rates of most neurons showed a variety of function according to sound frequency,especially at high intensities.These results strongly suggest that FSL of auditory IC neurons can represent sound frequency more precisely than spike rate.     

Latency represents sound frequency in mouse IC

Frequency is one of the fundamental parameters of sound. The frequency of an acoustic stimulus can be represented by a neural response such as spike rate, and/or first spike latency (FSL) of a given neuron. The spike rates/frequency function of most neurons changes with different acoustic amplitudes, whereas FSL/frequency function is highly stable. This implies that FSL might represent the frequency of a sound stimulus more efficiently than spike rate. This study involved representations of acoustic frequency by spike rate and FSL of central inferior colliculus (IC) neurons responding to free-field pure-tone stimuli. We found that the FSLs of neurons responding to characteristic frequency (CF) of sound stimulus were usually the shortest, regardless of sound intensity, and that spike rates of most neurons showed a variety of function according to sound frequency, especially at high intensities.These results strongly suggest that FSL of auditory IC neurons can represent sound frequency more precisely than spike rate.     

Columnar Connectivity and Laminar Processing in Cat Primary Auditory Cortex

Background

Radial intra- and interlaminar connections form a basic microcircuit in primary auditory cortex (AI) that extracts acoustic information and distributes it to cortical and subcortical networks. Though the structure of this microcircuit is known, we do not know how the functional connectivity between layers relates to laminar processing.

Methodology/Principal Findings

We studied the relationships between functional connectivity and receptive field properties in this columnar microcircuit by simultaneously recording from single neurons in cat AI in response to broadband dynamic moving ripple stimuli. We used spectrotemporal receptive fields (STRFs) to estimate the relationship between receptive field parameters and the functional connectivity between pairs of neurons. Interlaminar connectivity obtained through cross-covariance analysis reflected a consistent pattern of information flow from thalamic input layers to cortical output layers. Connection strength and STRF similarity were greatest for intralaminar neuron pairs and in supragranular layers and weaker for interlaminar projections. Interlaminar connection strength co-varied with several STRF parameters: feature selectivity, phase locking to the stimulus envelope, best temporal modulation frequency, and best spectral modulation frequency. Connectivity properties and receptive field relationships differed for vertical and horizontal connections.

Conclusions/Significance

Thus, the mode of local processing in supragranular layers differs from that in infragranular layers. Therefore, specific connectivity patterns in the auditory cortex shape the flow of information and constrain how spectrotemporal processing transformations progress in the canonical columnar auditory microcircuit.     

Influence of sound pressure level on the processing of amplitude modulations by auditory neurons of the locust

Typical features of natural sounds are amplitude changes at different time scales. In many species, amplitude modulations constitute decisive cues to recognize communication signals. Since these signals should be recognizable over a broad intensity range, we investigated how the encoding of amplitude modulations by auditory neurons depends on sound pressure level. Identified neurons that represent different processing stages in the locusts’ auditory pathway were stimulated with sinusoidal modulations of a broad band noise carrier, at different intensities, and characteristic parameters of modulation transfer functions (MTFs) were determined. The corner frequencies of temporal MTFs turned out to be independent of intensity for all neurons except one. Furthermore, for none of the neurons investigated corner frequencies were significantly correlated with spike rate, indicating a remarkable intensity invariance of the upper limits of temporal resolution. The shape of the tMTFs changed with increasing intensity from a low-pass to a band-pass for receptors and local neurons, while no consistent change was observed for ascending neurons. The best modulation frequency depended on intensity and spike rate, especially for receptors and local neurons. Remarkably, the adaptation state of some neurons turned out to be independent of the spike rate during the modulation part of the stimulus.     

The amplitude sensitivity of mouse inferior collicular neurons in the presence of weak noise

Natural auditory environment consists of multiple sound sources that are embedded in ambient strong and weak noise. For effective sound communication and signal analysis, animals must somehow extract biologically relevant signals from the inevitable interference of ambient noise. The present study examined how a weak noise may affect the amplitude sensitivity of neurons in the mouse central nucleus of the inferior colliculus (IC) which receives convergent excitatory and inhibitory inputs from both lower and higher auditory centers. Specifically, we studied the amplitude sensitivity of IC neurons using a probe (best frequency pulse) and a masker (weak noise) under simultaneous masking paradigm. For most IC neurons, weak noise masking increases the minimum threshold and decreases the number of impulses. Noise masking also increased the slope and decreased the dynamic range of the rate amplitude function of these IC neurons. The strength of this noise masking was greater at low than at high sound amplitudes. This variation in the amplitude sensitivity of IC neurons in the presence of the weak noise was mostly mediated through GABAergic inhibition. These data indicate that in the real world the ambient weak noise improves amplitude sensitivity of IC neurons through GABAergic inhibition while inevitably decreases the range of overall auditory sensitivity of IC neurons.     

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

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

听神经元的频率调谐

频率作为声音的一个重要参数,在听敏感神经元对声音进行分析和编码过程中扮演重要角色。一般用频率调谐曲线来表示听敏感神经元的频率调谐特性,并用Qn(10,30,50)值表达频率调谐曲线的尖锐程度,Qn值越大,频率调谐曲线也越尖锐,神经元的频率调谐能力越好,对频率的分辨能力越高。从听觉外周到中枢,听敏感神经元的频率调谐逐级锐化,而这种锐化主要是由听中枢的多种抑制性神经递质的作用而产生的,其中起主要作用的是GABA能和甘氨酸能神经递质。此外,离皮层调控,双侧下丘间的联合投射以及弱噪声前掩蔽等因素也会影响听敏感神经元的频率调谐特性。     

Auditory motion aftereffects of low- and high-frequency sound stimuli

Adaptation of listeners to approaching or receding sound stimuli continued for 5 s under free-field conditions. Motion of the adaptive and test sound stimuli was simulated by means of oppositely directed linear changes in the amplitude of the low- and high-frequency noises (0.05–1 and 3–20 kHz, respectively) from two stationary loudspeakers. In a group of eight subjects with normal hearing, the auditory motion after-effect of the approaching and receding sound stimuli was evaluated by integrated indices that characterized the shift of the psychometric curves in response to the test stimuli under various conditions of listening. The aftereffect occurs in the case when the spectral composition of the adaptive and test stimuli coincides. In response to the high-frequency stimuli, the effect of adaptation to both the approaching and receding sound stimuli was observed, while in response to the low-frequency stimuli, only the approach of stimuli caused an aftereffect. There was no radial motion aftereffect in the case of mismatching the spectral bands of the adaptive and test stimuli. Thus, the frequency selectivity was characteristic of the auditory aftereffect of adaptation to the approaching and receding sound stimuli.