Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings.

Following repetitive visual stimulation, post hoc phase analysis finds that visually evoked response magnitudes vary with the cortical alpha oscillation phase that temporally coincides with sensory stimulus. This approach has not successfully revealed an alpha phase dependence for auditory evoked or induced responses. Here, we test the feasibility of tracking alpha with scalp electroencephalogram (EEG) recordings and play sounds phase-locked to individualized alpha phases in real-time using a novel end-point corrected Hilbert transform (ecHT) algorithm implemented on a research device. Based on prior work, we hypothesize that sound-evoked and induced responses vary with the alpha phase at sound onset and the alpha phase that coincides with the early sound-evoked response potential (ERP) measured with EEG. Thus, we use each subject's individualized alpha frequency (IAF) and individual auditory ERP latency to define target trough and peak alpha phases that allow an early component of the auditory ERP to align to the estimated poststimulus peak and trough phases, respectively. With this closed-loop and individualized approach, we find opposing alpha phase-dependent effects on the auditory ERP and alpha oscillations that follow stimulus onset. Trough and peak phase-locked sounds result in distinct evoked and induced post-stimulus alpha level and frequency modulations. Though additional studies are needed to localize the sources underlying these phase-dependent effects, these results suggest a general principle for alpha phase-dependence of sensory processing that includes the auditory system. Moreover, this study demonstrates the feasibility of using individualized neurophysiological indices to deliver automated, closed-loop, phase-locked auditory stimulation.

A wearable EEG system for closed-loop neuromodulation of sleep-related oscillations.

Objective.Healthy sleep plays a critical role in general well-being. Enhancement of slow-wave sleep by targeting acoustic stimuli to particular phases of delta (0.5-2 Hz) waves has shown promise as a non-invasive approach to improve sleep quality. Closed-loop stimulation during other sleep phases targeting oscillations at higher frequencies such as theta (4-7 Hz) or alpha (8-12 Hz) could be another approach to realize additional health benefits. However, systems to track and deliver stimulation relative to the instantaneous phase of electroencephalogram (EEG) signals at these higher frequencies have yet to be demonstrated outside of controlled laboratory settings.Approach.Here we examine the feasibility of using an endpoint-corrected version of the Hilbert transform (ecHT) algorithm implemented on a headband wearable device to measure alpha phase and deliver phase-locked auditory stimulation during the transition from wakefulness to sleep, during which alpha power is greatest. First, the ecHT algorithm is implementedin silicoto evaluate the performance characteristics of this algorithm across a range of sleep-related oscillatory frequencies. Secondly, a pilot sleep study tests feasibility to use the wearable device by users in the home setting for measurement of EEG activity during sleep and delivery of real-time phase-locked stimulation.Main results.The ecHT is capable of computing the instantaneous phase of oscillating signals with high precision, allowing auditory stimulation to be delivered at the intended phases of neural oscillations with low phase error. The wearable system was capable of measuring sleep-related neural activity with sufficient fidelity for sleep stage scoring during the at-home study, and phase-tracking performance matched simulated results. Users were able to successfully operate the system independently using the companion smartphone app to collect data and administer stimulation, and presentation of auditory stimuli during sleep initiation did not negatively impact sleep onset.Significance.This study demonstrates the feasibility of closed-loop real-time tracking and neuromodulation of a range of sleep-related oscillations using a wearable EEG device. Preliminary results suggest that this approach could be used to deliver non-invasive neuromodulation across all phases of sleep.

Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures.

The perception of sound textures, a class of natural sounds defined by statistical sound structure such as fire, wind, and rain, has been proposed to arise through the integration of time-averaged summary statistics. Where and how the auditory system might encode these summary statistics to create internal representations of these stationary sounds, however, is unknown. Here, using natural textures and synthetic variants with reduced statistics, we show that summary statistics modulate the correlations between frequency organized neuron ensembles in the awake rabbit inferior colliculus (IC). These neural ensemble correlation statistics capture high-order sound structure and allow for accurate neural decoding in a single trial recognition task with evidence accumulation times approaching 1 s. In contrast, the average activity across the neural ensemble (neural spectrum) provides a fast (tens of milliseconds) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveal analogous trends: the sound spectrum is integrated quickly and serves as a salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more toward recognition. The findings suggest statistical sound cues such as the sound spectrum and correlation structure are represented by distinct response statistics in auditory midbrain ensembles, and that these neural response statistics may have dissociable roles and time scales for the recognition and discrimination of natural sounds.

Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces.

Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.

Modeling stimulus-dependent variability improves decoding of population neural responses.

OBJECTIVE: Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. APPROACH: In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models-the Conway-Maxwell-Poisson (CMP) model and negative binomial (NB) model-to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor > 1) or, for the CMP model, less than the mean (Fano factor < 1). MAIN RESULTS: We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%-12% compared to the Poisson model. SIGNIFICANCE: Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

A temporal integration mechanism enhances frequency selectivity of broadband inputs to inferior colliculus.

Accurately resolving frequency components in sounds is essential for sound recognition, yet there is little direct evidence for how frequency selectivity is preserved or newly created across auditory structures. We demonstrate that prepotentials (PPs) with physiological properties resembling presynaptic potentials from broadly tuned brainstem inputs can be recorded concurrently with postsynaptic action potentials in inferior colliculus (IC). These putative brainstem inputs (PBIs) are broadly tuned and exhibit delayed and spectrally interleaved excitation and inhibition not present in the simultaneously recorded IC neurons (ICNs). A sharpening of tuning is accomplished locally at the expense of spike-timing precision through nonlinear temporal integration of broadband inputs. A neuron model replicates the finding and demonstrates that temporal integration alone can degrade timing precision while enhancing frequency tuning through interference of spectrally in- and out-of-phase inputs. These findings suggest that, in contrast to current models that require local inhibition, frequency selectivity can be sharpened through temporal integration, thus supporting an alternative computational strategy to quickly refine frequency selectivity.

A Hierarchy of Time Scales for Discriminating and Classifying the Temporal Shape of Sound in Three Auditory Cortical Fields.

Auditory cortex is essential for mammals, including rodents, to detect temporal "shape" cues in the sound envelope but it remains unclear how different cortical fields may contribute to this ability (Lomber and Malhotra, 2008; Threlkeld et al., 2008). Previously, we found that precise spiking patterns provide a potential neural code for temporal shape cues in the sound envelope in the primary auditory (A1), and ventral auditory field (VAF) and caudal suprarhinal auditory field (cSRAF) of the rat (Lee et al., 2016). Here, we extend these findings and characterize the time course of the temporally precise output of auditory cortical neurons in male rats. A pairwise sound discrimination index and a Naive Bayesian classifier are used to determine how these spiking patterns could provide brain signals for behavioral discrimination and classification of sounds. We find response durations and optimal time constants for discriminating sound envelope shape increase in rank order with: A1 < VAF < cSRAF. Accordingly, sustained spiking is more prominent and results in more robust sound discrimination in non-primary cortex versus A1. Spike-timing patterns classify 10 different sound envelope shape sequences and there is a twofold increase in maximal performance when pooling output across the neuron population indicating a robust distributed neural code in all three cortical fields. Together, these results support the idea that temporally precise spiking patterns from primary and non-primary auditory cortical fields provide the necessary signals for animals to discriminate and classify a large range of temporal shapes in the sound envelope.SIGNIFICANCE STATEMENT Functional hierarchies in the visual cortices support the concept that classification of visual objects requires successive cortical stages of processing including a progressive increase in classical receptive field size. The present study is significant as it supports the idea that a similar progression exists in auditory cortices in the time domain. We demonstrate for the first time that three cortices provide temporal spiking patterns for robust temporal envelope shape discrimination but only the ventral non-primary cortices do so on long time scales. This study raises the possibility that primary and non-primary cortices provide unique temporal spiking patterns and time scales for perception of sound envelope shape.

Origins of scale invariance in vocalization sequences and speech.

To communicate effectively animals need to detect temporal vocalization cues that vary over several orders of magnitude in their amplitude and frequency content. This large range of temporal cues is evident in the power-law scale-invariant relationship between the power of temporal fluctuations in sounds and the sound modulation frequency (f). Though various forms of scale invariance have been described for natural sounds, the origins and implications of scale invariant phenomenon remain unknown. Using animal vocalization sequences, including continuous human speech, and a stochastic model of temporal amplitude fluctuations we demonstrate that temporal acoustic edges are the primary acoustic cue accounting for the scale invariant phenomenon. The modulation spectrum of vocalization sequences and the model both exhibit a dual regime lowpass structure with a flat region at low modulation frequencies and scale invariant 1/f2 trend for high modulation frequencies. Moreover, we find a time-frequency tradeoff between the average vocalization duration of each vocalization sequence and the cutoff frequency beyond which scale invariant behavior is observed. These results indicate that temporal edges are universal features responsible for scale invariance in vocalized sounds. This is significant since temporal acoustic edges are salient perceptually and the auditory system could exploit such statistical regularities to minimize redundancies and generate compact neural representations of vocalized sounds.

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields.

Mammals perceive a wide range of temporal cues in natural sounds, and the auditory cortex is essential for their detection and discrimination. The rat primary (A1), ventral (VAF), and caudal suprarhinal (cSRAF) auditory cortical fields have separate thalamocortical pathways that may support unique temporal cue sensitivities. To explore this, we record responses of single neurons in the three fields to variations in envelope shape and modulation frequency of periodic noise sequences. Spike rate, relative synchrony, and first-spike latency metrics have previously been used to quantify neural sensitivities to temporal sound cues; however, such metrics do not measure absolute spike timing of sustained responses to sound shape. To address this, in this study we quantify two forms of spike-timing precision, jitter, and reliability. In all three fields, we find that jitter decreases logarithmically with increase in the basis spline (B-spline) cutoff frequency used to shape the sound envelope. In contrast, reliability decreases logarithmically with increase in sound envelope modulation frequency. In A1, jitter and reliability vary independently, whereas in ventral cortical fields, jitter and reliability covary. Jitter time scales increase (A1 < VAF < cSRAF) and modulation frequency upper cutoffs decrease (A1 > VAF > cSRAF) with ventral progression from A1. These results suggest a transition from independent encoding of shape and periodicity sound cues on short time scales in A1 to a joint encoding of these same cues on longer time scales in ventral nonprimary cortices.

A high-density, high-channel count, multiplexed µECoG array for auditory-cortex recordings.

Our understanding of the large-scale population dynamics of neural activity is limited, in part, by our inability to record simultaneously from large regions of the cortex. Here, we validated the use of a large-scale active microelectrode array that simultaneously records 196 multiplexed micro-electrocortigraphical (µECoG) signals from the cortical surface at a very high density (1,600 electrodes/cm(2)). We compared µECoG measurements in auditory cortex using a custom "active" electrode array to those recorded using a conventional "passive" µECoG array. Both of these array responses were also compared with data recorded via intrinsic optical imaging, which is a standard methodology for recording sound-evoked cortical activity. Custom active µECoG arrays generated more veridical representations of the tonotopic organization of the auditory cortex than current commercially available passive µECoG arrays. Furthermore, the cortical representation could be measured efficiently with the active arrays, requiring as little as 13.5 s of neural data acquisition. Next, we generated spectrotemporal receptive fields from the recorded neural activity on the active µECoG array and identified functional organizational principles comparable to those observed using intrinsic metabolic imaging and single-neuron recordings. This new electrode array technology has the potential for large-scale, temporally precise monitoring and mapping of the cortex, without the use of invasive penetrating electrodes.

Novel acoustic stimuli can alter locomotor speed to hippocampal theta relationship.

Hippocampal theta (6-12 Hz) plays a critical role in synchronizing the discharge of action potentials, ultimately orchestrating individual neurons into large-scale ensembles. Alterations in theta dynamics may reflect variations in sensorimotor integration, the flow of sensory input, and/or cognitive processing. Previously we have investigated septotemporal variation in the locomotor speed to theta amplitude relationship as well as how that relationship is systematically altered as a function of novel, physical space. In the present study, we ask, beyond physical space, whether persistent and passive sound delivery can alter septal theta local field potential rhythm dynamics. Results indicate pronounced alterations in the slope of the speed to theta amplitude relationship as a function of sound presentation and location. Further, this reduction in slope habituates across days. The current findings highlight that moment-to-moment alterations in theta amplitude is a rich dynamic index that is quantitatively related to both alterations in motor behavior and sensory experience. The implications of these phenomena are discussed with respect to emergent cognitive functions subserved by hippocampal circuits.

Gene expression identifies distinct ascending glutamatergic pathways to frequency-organized auditory cortex in the rat brain.

A conserved feature of sound processing across species is the presence of multiple auditory cortical fields with topographically organized responses to sound frequency. Current organizational schemes propose that the ventral division of the medial geniculate body (MGBv) is a single functionally homogenous structure that provides the primary source of input to all neighboring frequency-organized cortical fields. These schemes fail to account for the contribution of MGBv to functional diversity between frequency-organized cortical fields. Here, we report response property differences for two auditory fields in the rat, and find they have nonoverlapping sources of thalamic input from the MGBv that are distinguished by the gene expression for type 1 vesicular glutamate transporter. These data challenge widely accepted organizational schemes and demonstrate a genetic plurality in the ascending glutamatergic pathways to frequency-organized auditory cortex.

Spectrotemporal sound preferences of neighboring inferior colliculus neurons: implications for local circuitry and processing.

How do local circuits in the inferior colliculus (IC) process and transform spectral and temporal sound information? Using a four-tetrode array we examined the functional properties of the IC and metrics of its micro circuitry by recording neural activity from neighboring single neurons in the cat. Spectral and temporal response preferences were compared for neurons found on the same and adjacent tetrodes (ATs), as well as across distant recording sites. We found that neighboring neurons had similar preferences while neurons recorded across distant sites were less similar. Best frequency (BF) was the most correlated parameter between neighboring neurons and BF differences exhibited unique clustering at ~0.3 octave intervals, indicative of the frequency band lamina. Other spectral and temporal parameters of the receptive fields were more similar for neighboring neurons than for those at distant sites and the receptive field similarity was larger for neurons with small differences in BF. Furthermore, correlated firing was stronger for neighboring neuron pairs and increased with proximity and decreasing BF difference. Thus, although response selectivities are quite diverse in the IC, spectral, and temporal preference within a local microcircuit are functionally quite similar. This suggests a scheme where local circuits are organized into zones that are specialized for processing distinct spectrotemporal cues.

Precise feature based time scales and frequency decorrelation lead to a sparse auditory code.

Sparse redundancy reducing codes have been proposed as efficient strategies for representing sensory stimuli. A prevailing hypothesis suggests that sensory representations shift from dense redundant codes in the periphery to selective sparse codes in cortex. We propose an alternative framework where sparseness and redundancy depend on sensory integration time scales and demonstrate that the central nucleus of the inferior colliculus (ICC) of cats encodes sound features by precise sparse spike trains. Direct comparisons with auditory cortical neurons demonstrate that ICC responses were sparse and uncorrelated as long as the spike train time scales were matched to the sensory integration time scales relevant to ICC neurons. Intriguingly, correlated spiking in the ICC was substantially lower than predicted by linear or nonlinear models and strictly observed for neurons with best frequencies within a "critical band," the hallmark of perceptual frequency resolution in mammals. This is consistent with a sparse asynchronous code throughout much of the ICC and a complementary correlation code within a critical band that may allow grouping of perceptually relevant cues.

Thalamocortical pathway specialization for sound frequency resolution.

Core auditory cortices are organized in parallel pathways that process incoming sensory information differently. In the rat, sound filtering properties of the primary (A1) and ventral (VAF) auditory fields are markedly different, yet both are core regions that by definition receive most of their thalamic input from the ventral nucleus (MGBv) of the medial geniculate body (MGB). For example, spike rate responses to sound intensity and frequency are more narrowly resolved in VAF vs. A1. Here we question whether there are anatomic correlates of the marked differences in response properties in these two core auditory fields. Combined Fourier optical imaging and multiunit recording methods were used to map tone frequency responses with high spatial resolution in A1, VAF, and neighboring cortices. The cortical distance representing a given octave was similar, yet response frequency resolution was about twice as large in VAF as in A1. Retrograde tracers were injected into low- and high-isofrequency contours of both regions to compare MGBv label patterns. The distance between clusters of MGBv neurons projecting to low- and high-isofrequency contours in the cortex was twice as large in caudal as in rostral MGB. This suggests that differences in A1 and VAF frequency resolution are related to the anatomic spatial resolution of frequency laminae in the thalamus, supporting a growing consensus that antecedents of cortical specialization can be attributed in part to the structural and functional characteristics of thalamocortical inputs.

Distinct core thalamocortical pathways to central and dorsal primary auditory cortex.

The cat primary auditory cortex (AI) is usually assumed to form one continuous functional region. However, the dorsal and central parts of the AI iso-frequency domain contain neurons that have distinct response properties to acoustic stimuli. In this study, we asked whether neurons projecting to dorsal versus central regions of AI originate in different parts of the medial geniculate body (MGB). Spike rate responses to variations in the sound level and frequency of pure tones were used to measure characteristic frequency (CF) and frequency resolution. These were mapped with high spatial density in order to place retrograde tracers into matching frequency regions of the central narrow-band region (cNB) and dorsal AI. Labeled neurons projecting to these two parts of AI were concentrated in the middle and rostral thirds of the MGB, respectively. There was little evidence that differences in dorsal and central AI function could be due to convergent input from cells outside the ventral division of the MGB (MGBv). Instead, inputs arising from different locations along the caudal-to-rostral dimension of MGBv represent potential sources of response differences between central and dorsal sub-regions of AI.

Neural modulation tuning characteristics scale to efficiently encode natural sound statistics.

The efficient-coding hypothesis asserts that neural and perceptual sensitivity evolved to faithfully represent biologically relevant sensory signals. Here we characterized the spectrotemporal modulation statistics of several natural sound ensembles and examined how neurons encode these statistics in the central nucleus of the inferior colliculus (CNIC) of cats. We report that modulation-tuning in the CNIC is matched to equalize the modulation power of natural sounds. Specifically, natural sounds exhibited a tradeoff between spectral and temporal modulations, which manifests as 1/f modulation power spectrum (MPS). Neural tuning was highly overlapped with the natural sound MPS and neurons approximated proportional resolution filters where modulation bandwidths scaled with characteristic modulation frequencies, a behavior previously described in human psychoacoustics. We demonstrate that this neural scaling opposes the 1/f scaling of natural sounds and enhances the natural sound representation by equalizing their MPS. Modulation tuning in the CNIC may thus have evolved to represent natural sound modulations in a manner consistent with efficiency principles and the resulting characteristics likely underlie perceptual resolution.

Specialization of binaural responses in ventral auditory cortices.

Accurate orientation to sound under challenging conditions requires auditory cortex, but it is unclear how spatial attributes of the auditory scene are represented at this level. Current organization schemes follow a functional division whereby dorsal and ventral auditory cortices specialize to encode spatial and object features of sound source, respectively. However, few studies have examined spatial cue sensitivities in ventral cortices to support or reject such schemes. Here Fourier optical imaging was used to quantify best frequency responses and corresponding gradient organization in primary (A1), anterior, posterior, ventral (VAF), and suprarhinal (SRAF) auditory fields of the rat. Spike rate sensitivities to binaural interaural level difference (ILD) and average binaural level cues were probed in A1 and two ventral cortices, VAF and SRAF. Continuous distributions of best ILDs and ILD tuning metrics were observed in all cortices, suggesting this horizontal position cue is well covered. VAF and caudal SRAF in the right cerebral hemisphere responded maximally to midline horizontal position cues, whereas A1 and rostral SRAF responded maximally to ILD cues favoring more eccentric positions in the contralateral sound hemifield. SRAF had the highest incidence of binaural facilitation for ILD cues corresponding to midline positions, supporting current theories that auditory cortices have specialized and hierarchical functional organization.

Thalamic label patterns suggest primary and ventral auditory fields are distinct core regions.

A hierarchical scheme proposed by Kaas and colleagues suggests that primate auditory cortex can be divided into core and belt regions based on anatomic connections with thalamus and distinctions among response properties. According to their model, core auditory cortex receives predominantly unimodal sensory input from the ventral nucleus of the medial geniculate body (MGBv); whereas belt cortex receives predominantly cross-modal sensory input from nuclei outside the MGBv. We previously characterized distinct response properties in rat primary (A1) versus ventral auditory field (VAF) cortex; however, it has been unclear whether VAF should be categorized as a core or belt auditory cortex. The current study employed high-resolution functional imaging to map intrinsic metabolic responses to tones and to guide retrograde tracer injections into A1 and VAF. The size and density of retrogradely labeled somas in the medial geniculate body (MGB) were examined as a function of their position along the caudal-to-rostral axis, subdivision of origin, and cortical projection target. A1 and VAF projecting neurons were found in the same subdivisions of the MGB but in rostral and caudal parts, respectively. Less than 3% of the cells projected to both regions. VAF projecting neurons were smaller than A1 projecting neurons located in dorsal (MGBd) and suprageniculate (SG) nuclei. Thus, soma size varied with both caudal-rostral position and cortical target. Finally, the majority (>70%) of A1 and VAF projecting neurons were located in MGBv. These MGB connection profiles suggest that rat auditory cortex, like primate auditory cortex, is made up of multiple distinct core regions.

Spectral and temporal modulation tradeoff in the inferior colliculus.

The cochlea encodes sounds through frequency-selective channels that exhibit low-pass modulation sensitivity. Unlike the cochlea, neurons in the auditory midbrain are tuned for spectral and temporal modulations found in natural sounds, yet the role of this transformation is not known. We report a distinct tradeoff in modulation sensitivity and tuning that is topographically ordered within the central nucleus of the inferior colliculus (CNIC). Spectrotemporal receptive fields (STRFs) were obtained with 16-channel electrodes inserted orthogonal to the isofrequency lamina. Surprisingly, temporal and spectral characteristics exhibited an opposing relationship along the tonotopic axis. For low best frequencies (BFs), units were selective for fast temporal and broad spectral modulations. A systematic progression was observed toward slower temporal and finer spectral modulation sensitivity at high BF. This tradeoff was strongly reflected in the arrangement of excitation and inhibition and, consequently, in the modulation tuning characteristics. Comparisons with auditory nerve fibers show that these trends oppose the pattern imposed by the peripheral filters. These results suggest that spectrotemporal preferences are reordered within the tonotopic axis of the CNIC. This topographic organization has profound implications for the coding of spectrotemporal features in natural sounds and could underlie a number of perceptual phenomena.