Ferret Primary Auditory Cortex Research Articles

Task-specific invariant representation in auditory cortex.

Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher-order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in the ferret primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.

Reversible Inactivation of Ferret Auditory Cortex Impairs Spatial and Nonspatial Hearing.

A key question in auditory neuroscience is to what extent are brain regions functionally specialized for processing specific sound features, such as location and identity. In auditory cortex, correlations between neural activity and sounds support both the specialization of distinct cortical subfields, and encoding of multiple sound features within individual cortical areas. However, few studies have tested the contribution of auditory cortex to hearing in multiple contexts. Here we determined the role of ferret primary auditory cortex in both spatial and nonspatial hearing by reversibly inactivating the middle ectosylvian gyrus during behavior using cooling (n = 2 females) or optogenetics (n = 1 female). Optogenetic experiments used the mDLx promoter to express Channelrhodopsin-2 in GABAergic interneurons, and we confirmed both viral expression (n = 2 females) and light-driven suppression of spiking activity in auditory cortex, recorded using Neuropixels under anesthesia (n = 465 units from 2 additional untrained female ferrets). Cortical inactivation via cooling or optogenetics impaired vowel discrimination in colocated noise. Ferrets implanted with cooling loops were tested in additional conditions that revealed no deficit when identifying vowels in clean conditions, or when the temporally coincident vowel and noise were spatially separated by 180 degrees. These animals did, however, show impaired sound localization when inactivating the same auditory cortical region implicated in vowel discrimination in noise. Our results demonstrate that, as a brain region showing mixed selectivity for spatial and nonspatial features of sound, primary auditory cortex contributes to multiple forms of hearing.SIGNIFICANCE STATEMENT Neurons in primary auditory cortex are often sensitive to the location and identity of sounds. Here we inactivated auditory cortex during spatial and nonspatial listening tasks using cooling, or optogenetics. Auditory cortical inactivation impaired multiple behaviors, demonstrating a role in both the analysis of sound location and identity and confirming a functional contribution of mixed selectivity observed in neural activity. Parallel optogenetic experiments in two additional untrained ferrets linked behavior to physiology by demonstrating that expression of Channelrhodopsin-2 permitted rapid light-driven suppression of auditory cortical activity recorded under anesthesia.

General properties of auditory spectro-temporal receptive fields.

The analyses of more than 2000 spectro-temporal receptive fields (STRFs) obtained from the ferret primary auditory cortex revealed their dominant encoding properties along time and frequency domains. Results showed that the peak responses of the STRFs were roughly aligned along the time axis, and enhanced low modulation components of the signal around 3 Hz. On the contrary, the peaks of the STRF population along the frequency axis varied and were distributed widely. Further analyses revealed that there were some general properties along the frequency axis around the best frequency of each STRF. The STRFs enhanced modulation frequencies around 0.25 cycles/octave. These findings are consistent with techniques that have been reported in the literature to result in improvements in speech recognition performance, highlighting the significant potential of biological insights into the design of better machines.

Laminar profile of task-related plasticity in ferret primary auditory cortex

Rapid task-related plasticity is a neural correlate of selective attention in primary auditory cortex (A1). Top-down feedback from higher-order cortex may drive task-related plasticity in A1, characterized by enhanced neural representation of behaviorally meaningful sounds during auditory task performance. Since intracortical connectivity is greater within A1 layers 2/3 (L2/3) than in layers 4–6 (L4–6), we hypothesized that enhanced representation of behaviorally meaningful sounds might be greater in A1 L2/3 than L4–6. To test this hypothesis and study the laminar profile of task-related plasticity, we trained 2 ferrets to detect pure tones while we recorded laminar activity across a 1.8 mm depth in A1. In each experiment we analyzed high-gamma local field potentials (LFPs) and multi-unit spiking in response to identical acoustic stimuli during both passive listening and active task performance. We found that neural responses to auditory targets were enhanced during task performance, and target enhancement was greater in L2/3 than in L4–6. Spectrotemporal receptive fields (STRFs) computed from both high-gamma LFPs and multi-unit spiking showed similar increases in auditory target selectivity, also greatest in L2/3. Our results suggest that activity within intracortical networks plays a key role in the underlying neural mechanisms of selective attention.

Real-time Algorithms for Sparse Neuronal System Identification.

We consider the problem of sparse adaptive neuronal system identification, where the goal is to estimate the sparse time-varying neuronal model parameters in an online fashion from neural spiking observations. We develop two adaptive filters based on greedy estimation techniques and regularized log-likelihood maximization. We apply the proposed algorithms to simulated spiking data as well as experimentally recorded data from the ferret's primary auditory cortex during performance of auditory tasks. Our results reveal significant performance gains achieved by the proposed algorithms in terms of sparse identification and trackability, compared to existing algorithms.

Recursive Sparse Point Process Regression With Application to Spectrotemporal Receptive Field Plasticity Analysis

We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ $\ell_1$-regularization to capture the sparsity. We provide a rigorous analysis of the maximizers of the objective function, which extends the guarantees of compressed sensing to our setting. We construct two recursive filters for online estimation of the parameter vectors based on proximal optimization techniques, as well as a novel filter for recursive computation of statistical confidence regions. Simulation studies reveal that our algorithms outperform several existing point process filters in terms of trackability, goodness-of-fit and mean square error. We finally apply our filtering algorithms to experimentally recorded spiking data from the ferret primary auditory cortex during attentive behavior in a click rate discrimination task. Our analysis provides new insights into the time-course of the spectrotemporal receptive field plasticity of the auditory neurons.

Mechanisms of noise robust representation of speech in primary auditory cortex.

Humans and animals can reliably perceive behaviorally relevant sounds in noisy and reverberant environments, yet the neural mechanisms behind this phenomenon are largely unknown. To understand how neural circuits represent degraded auditory stimuli with additive and reverberant distortions, we compared single-neuron responses in ferret primary auditory cortex to speech and vocalizations in four conditions: clean, additive white and pink (1/f) noise, and reverberation. Despite substantial distortion, responses of neurons to the vocalization signal remained stable, maintaining the same statistical distribution in all conditions. Stimulus spectrograms reconstructed from population responses to the distorted stimuli resembled more the original clean than the distorted signals. To explore mechanisms contributing to this robustness, we simulated neural responses using several spectrotemporal receptive field models that incorporated either a static nonlinearity or subtractive synaptic depression and multiplicative gain normalization. The static model failed to suppress the distortions. A dynamic model incorporating feed-forward synaptic depression could account for the reduction of additive noise, but only the combined model with feedback gain normalization was able to predict the effects across both additive and reverberant conditions. Thus, both mechanisms can contribute to the abilities of humans and animals to extract relevant sounds in diverse noisy environments.

Integration over Multiple Timescales in Primary Auditory Cortex

Speech and other natural vocalizations are characterized by large modulations in their sound envelope. The timing of these modulations contains critical information for discrimination of important features, such as phonemes. We studied how depression of synaptic inputs, a mechanism frequently reported in cortex, can contribute to the encoding of envelope dynamics. Using a nonlinear stimulus-response model that accounted for synaptic depression, we predicted responses of neurons in ferret primary auditory cortex (A1) to stimuli with natural temporal modulations. The depression model consistently performed better than linear and second-order models previously used to characterize A1 neurons, and it produced more biologically plausible fits. To test how synaptic depression can contribute to temporal stimulus integration, we used nonparametric maximum a posteriori decoding to compare the ability of neurons showing and not showing depression to reconstruct the stimulus envelope. Neurons showing evidence for depression reconstructed stimuli over a longer range of latencies. These findings suggest that variation in depression across the cortical population supports a rich code for representing the temporal dynamics of natural sounds.

Surprising Twist on Auditory Representation. Focus on: “What's That Sound? Auditory Area CLM Encodes Stimulus Surprise, Not Intensity or Intensity Changes”

The concept of sensory representation is central to psychological theories of perception and cognition and to neurobiological models of sensory coding. Understanding exactly what a neural response “represents,” however, turns out to be a difficult question to answer fully—particularly for neurons many synapses away from the point of sensory transduction. A recent paper by Gill and colleagues (2007) takes a large step forward in understanding stimulus representation in the auditory system. To appreciate the contribution this paper makes, it’s helpful to have a little background. To a first approximation, sensory systems and the receptive fields they comprise are organized hierarchically. The concept of a “receptive field” was first applied to the visual system (Hartline 1938) to describe the sensitivity of optic nerve fibers to light shined on spatially restricted portions of the retina. While neurons in primary visual cortex inherit the spatial receptive fields of the retinal projections, they respond to oriented bars or edges rather than spots of light. At higher levels in the visual pathway, cells respond best to complex

Physiological and behavioral studies of spatial coding in the auditory cortex

Despite extensive subcortical processing, the auditory cortex is believed to be essential for normal sound localization. However, we still have a poor understanding of how auditory spatial information is encoded in the cortex and of the relative contribution of different cortical areas to spatial hearing. We investigated the behavioral consequences of inactivating ferret primary auditory cortex (A1) on auditory localization by implanting a sustained release polymer containing the GABA A agonist muscimol bilaterally over A1. Silencing A1 led to a reversible deficit in the localization of brief noise bursts in both the horizontal and vertical planes. In other ferrets, large bilateral lesions of the auditory cortex, which extended beyond A1, produced more severe and persistent localization deficits. To investigate the processing of spatial information by high-frequency A1 neurons, we measured their binaural-level functions and used individualized virtual acoustic space stimuli to record their spatial receptive fields (SRFs) in anesthetized ferrets. We observed the existence of a continuum of response properties, with most neurons preferring contralateral sound locations. In many cases, the SRFs could be explained by a simple linear interaction between the acoustical properties of the head and external ears and the binaural frequency tuning of the neurons. Azimuth response profiles recorded in awake ferrets were very similar and further analysis suggested that the slopes of these functions and location-dependent variations in spike timing are the main information-bearing parameters. Studies of sensory plasticity can also provide valuable insights into the role of different brain areas and the way in which information is represented within them. For example, stimulus-specific training allows accurate auditory localization by adult ferrets to be relearned after manipulating binaural cues by occluding one ear. Reversible inactivation of A1 resulted in slower and less complete adaptation than in normal controls, whereas selective lesions of the descending corticocollicular pathway prevented any improvement in performance. These results reveal a role for auditory cortex in training-induced plasticity of auditory localization, which could be mediated by descending cortical pathways.

Development of contralateral and ipsilateral frequency representations in ferret primary auditory cortex

Little is known about the maturation of functional maps in the primary auditory cortex (A1) after the onset of sensory experience. We used intrinsic signal imaging to examine the development of the tonotopic organization of ferret A1 with respect to contralateral and ipsilateral tone stimulation. Sound-evoked responses were recorded as early as postnatal day (P) 33, a few days after hearing onset. From P36 onwards, pure tone stimuli evoked restricted, tonotopically organized patches of activity. There was an age-dependent increase in the cortical area representing each octave, with a disproportionate expansion of cortical territory representing frequencies > 4 kHz after P60. Similar tonotopic maps were observed following stimulation of the contralateral and ipsilateral ears. During the first few weeks following hearing onset, no differences were found in the area of cortical activation or in the magnitude of the optical responses evoked by stimulation of each ear. In older animals, however, contralateral stimuli evoked stronger responses and activated a larger A1 area than ipsilateral stimuli. Our findings indicate that neither the tonotopic organization nor the representation of inputs from each ear reach maturity until approximately 1 month after hearing onset. These results have important implications for cortical signal processing in juvenile animals.

Differential Dynamic Plasticity of A1 Receptive Fields during Multiple Spectral Tasks

Auditory experience leads to myriad changes in processing in the central auditory system. We recently described task-related plasticity characterized by rapid modulation of spectro-temporal receptive fields (STRFs) in ferret primary auditory cortex (A1) during tone detection. We conjectured that each acoustic task may have its own "signature" STRF changes, dependent on the salient cues that the animal must attend to perform the task. To discover whether other acoustic tasks could elicit changes in STRF shape, we recorded from A1 in ferrets also trained on a frequency discrimination task. Overall, we found a distinct pattern of STRF change, characterized by an expected selective enhancement at target tone frequency but also by an equally selective depression at reference tone frequency. When single-tone detection and frequency discrimination tasks were performed sequentially, neurons responded differentially to identical tones, reflecting distinct predictive values of stimuli in the two behavioral contexts. All results were observed in multiunit as well as single-unit recordings. Our findings provide additional evidence for the presence of adaptive neuronal responses in A1 that can swiftly change to reflect both sensory content and the changing behavioral meaning of incoming acoustic stimuli.

Encoding of Virtual Acoustic Space Stimuli by Neurons in Ferret Primary Auditory Cortex

Recent studies from our laboratory have indicated that the spatial response fields (SRFs) of neurons in the ferret primary auditory cortex (A1) with best frequencies > or =4 kHz may arise from a largely linear processing of binaural level and spectral localization cues. Here we extend this analysis to investigate how well the linear model can predict the SRFs of neurons with different binaural response properties and the manner in which SRFs change with increases in sound level. We also consider whether temporal features of the response (e.g., response latency) vary with sound direction and whether such variations can be explained by linear processing. In keeping with previous studies, we show that A1 SRFs, which we measured with individualized virtual acoustic space stimuli, expand and shift in direction with increasing sound level. We found that these changes are, in most cases, in good agreement with predictions from a linear threshold model. However, changes in spatial tuning with increasing sound level were generally less well predicted for neurons whose binaural frequency-time receptive field (FTRF) exhibited strong excitatory inputs from both ears than for those in which the binaural FTRF revealed either a predominantly inhibitory effect or no clear contribution from the ipsilateral ear. Finally, we found (in agreement with other authors) that many A1 neurons exhibit systematic response latency shifts as a function of sound-source direction, although these temporal details could usually not be predicted from the neuron's binaural FTRF.

Listening through different ears alters spatial response fields in ferret primary auditory cortex.

The localization of sounds in space is based on spatial cues that arise from the acoustical properties of the head and external ears. Individual differences in localization cue values result from variability in the shape and dimensions of these structures. We have mapped spatial response fields of high-frequency neurons in ferret primary auditory cortex using virtual sound sources based either on the animal's own ears or on the ears of other subjects. For 73% of units, the response fields measured using the animals' own ears differed significantly in shape and/or position from those obtained using spatial cues from another ferret. The observed changes correlated with individual differences in the acoustics. These data are consistent with previous reports showing that humans localize less accurately when listening to virtual sounds from other individuals. Together these findings support the notion that neural mechanisms underlying auditory space perception are calibrated by experience to the properties of the individual.

Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex.

To understand the neural representation of broadband, dynamic sounds in primary auditory cortex (AI), we characterize responses using the spectro-temporal response field (STRF). The STRF describes, predicts, and fully characterizes the linear dynamics of neurons in response to sounds with rich spectro-temporal envelopes. It is computed from the responses to elementary "ripples," a family of sounds with drifting sinusoidal spectral envelopes. The collection of responses to all elementary ripples is the spectro-temporal transfer function. The complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples. Previous experiments using ripples with downward drifting spectra suggested that the transfer function is separable, i.e., it is reducible into a product of purely temporal and purely spectral functions. Here we measure the responses to upward and downward drifting ripples, assuming reparability within each direction, to determine if the total bidirectional transfer function is fully separable. In general, the combined transfer function for two directions is not symmetric, and hence units in AI are not, in general, fully separable. Consequently, many AI units have complex response properties such as sensitivity to direction of motion, though most inseparable units are not strongly directionally selective. We show that for most neurons, the lack of full separability stems from differences between the upward and downward spectral cross-sections but not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.

How Plastic Is Spatial Hearing?

The location of a sound source is derived by the auditory system from spatial cues present in the signals at the two ears. These cues include interaural timing and level differences, as well as monaural spectral cues generated by the external ear. The values of these cues vary with individual differences in the shape and dimensions of the head and external ears. We have examined the neurophysiological consequences of these intersubject variations by recording the responses of neurons in ferret primary auditory cortex to virtual sound sources mimicking the animal’s own ears or those of other ferrets. For most neurons, the structure of the spatial response fields changed significantly when acoustic cues measured from another animal were presented. This is consistent with the finding that humans localize less accurately when listening to virtual sounds from other subjects. To examine the role of experience in shaping the ability to localize sound, we have studied the behavioural consequences of altering binaural cues by chronically plugging one ear. Ferrets raised and tested with one ear plugged learned to localize as accurately as control animals, which is consistent with previous findings that the representation of auditory space in the midbrain can accommodate abnormal sensory cues during development. Adaptive changes in behaviour were also observed in adults, particularly if they were provided with regular practice in the localization task. Together, these findings suggest that the neural circuits responsible for sound localization can be recalibrated throughout life.

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).

Callosal connections of the ferret primary auditory cortex.

The callosal connections of ferret auditory cortex were studied by making multiple injections of wheat germ agglutinin-horseradish peroxidase into the middle ectosylvian gyrus or by packing crystals of horseradish peroxidase into the transected corpus callosum. The primary area (AI) had strong callosal connections that arose from somata mainly located in layer III. Other layers contained sparsely distributed cells that projected across the midline. The projecting cells occurred over the whole extent of AI but were not homogeneously distributed in layer III. The axons from these cells terminated mainly in the upper layers of the contralateral cortex, where they converged onto three discrete bands. The three elongated bands lay in a dorsoventral orientation, parallel to the tonotopic axis. They were slightly curved and had a fairly uniform width. The posterior band had a width of about 200 microm, while the anterior and middle bands were more variable and had widths of 300-800 microm. The centre-to-centre distance between the posterior and middle bands was 520+/-60 microm and for the anterior to middle bands was 620+/-210 microm. The retrograde labelling produced by the same injections showed that the cell bodies had a higher density in the terminal bands than in the intervening spaces. The bands of dense callosal connections appear to correspond to the binaural summation columns, which have been clearly demonstrated in the ferret, but direct evidence of this will need to be sought in a future study. The discrete nature of the callosal bands in the ferret appears to make it a suitable species for studying the relationship between callosal terminals and those arising in other areas of the brain and for clarifying the possible existence of separate functional systems within the auditory cortex.

Analysis of dynamic spectra in ferret primary auditory cortex. I. Characteristics of single-unit responses to moving ripple spectra.

1. Auditory stimuli referred to as moving ripples are used to characterize the responses of both single and multiple units in the ferret primary auditory cortex. Moving ripples are broadband complex sounds with a sinusoidal spectral profile that drift along the logarithmic frequency axis at a constant velocity. 2. Neuronal responses to moving ripples are locked to the phase of the ripple, i.e., they exhibit the same periodicity as that of the moving ripple profile. Neural responses are characterized as a function of ripple velocity (temporal property) and ripple frequency (spectral property). Transfer functions describing the response to these temporal and spectral modulations are constructed. Temporal transfer functions are inverse Fourier transformed to obtain impulse response functions that reflect the cell's temporal characteristics. Ripple transfer functions are inverse Fourier transformed to obtain the response field, a measure analogous to the cell's response area. These operations assume linearity in the cell's response to moving ripples. 3. Transfer functions and other response functions are shown to be fairly independent on the overall level or depth of modulation of the ripple stimuli. Only downward moving ripples were used in this study. 4. The temporal and ripple transfer functions are found to be separable, in that their shapes remain unchanged for different test parameters. Thus ripple transfer functions and response fields remain statistically similar in shape (to within an overall scale factor) regardless of the ripple velocity or whether stationary or moving ripples are used in the measurement. The same stability in shape holds for the temporal transfer functions and the impulse response functions measured with different ripple frequencies. Separability implies that the combined spectrotemporal transfer function of a cell can be written as the product of a purely ripple and a purely temporal transfer functions, and thus that the neuron can be computationally modeled as processing spectral and temporal information in two separate and successive stages. 5. The ripple parameters that characterize cortical cells are distributed somewhat evenly, with the characteristic ripple frequencies ranging from 0.2 to > 2 cycles/octave and the characteristic angular frequency typically ranging from 2 to 20 Hz. 6. Many responses exhibit periodicities in the spectral envelope of the stimulus. These periodicities are of two types. Slow rebounds, not found in the spectral envelope, and with a period of approximately 150 ms, appear with various strengths in approximately 30% of the cells. Fast regular firings with interspike intervals of approximately 10 ms are much less common and appear to correspond to interactions between the component tones that make up a ripple.

Analysis of dynamic spectra in ferret primary auditory cortex. II. Prediction of unit responses to arbitrary dynamic spectra.

1. Responses of single units and multiunit clusters were recorded in the ferret primary auditory cortex (AI) with the use of broadband complex dynamic spectra. Previous work has demonstrated that simpler spectra consisting of single moving ripples (i.e., sinusoidally modulated spectral profiles that travel at a constant velocity along the logarithmic frequency axis) could be used effectively to characterize the response fields and transfer functions of AI cells. 2. A complex dynamic spectral profile can be thought of as being the sum of moving ripple spectra. Such a decomposition can be computed from a two-dimensional spectrotemporal Fourier transform of the dynamic spectral profile with moving ripples as the basis function. 3. Therefore, if AI units were essentially linear, satisfying the superposition principle, then their responses to arbitrary dynamic spectra could be predicted from the responses to single moving ripples, i.e., from the units' response fields and transfer functions (spectral and temporal impulse response functions, respectively). 4. This conjecture was tested and confirmed with data from 293 combinations of moving ripples, involving complex spectra composed of up to 15 moving ripples of different ripple frequencies and velocities. For each case, response predictions based on the unit transfer functions were compared with measured responses. The correlation between predicted and measured responses was found to be consistently high (84% with rho > 0.6). 5. The distribution of response parameters suggests that AI cells may encode the profile of a dynamic spectrum by performing a multiscale spectrotemporal decomposition of the dynamic spectral profile in a largely linear manner.