Gabor analysis of auditory midbrain receptive fields: spectro-temporal and binaural composition

Temporal symmetry in primary auditory cortex: implications for cortical connectivity

Neurons in primary auditory cortex (AI) in the ferret (Mustela putorius) that are well described by their spectrotemporal response field (STRF) are found also to have a distinctive property that we call temporal symmetry. For temporally symmetric neurons, every temporal cross-section of the STRF (impulse response) is given by the same function of time, except for a scaling and a Hilbert rotation. This property held in 85% of neurons (123 out of 145) recorded from awake animals and in 96% of neurons (70 out of 73) recorded from anesthetized animals. This property of temporal symmetry is highly constraining for possible models of functional neural connectivity within and into AI. We find that the simplest models of functional thalamic input, from the ventral medial geniculate body (MGB), into the entry layers of AI are ruled out because they are incompatible with the constraints of the observed temporal symmetry. This is also the case for the simplest models of functional intracortical connectivity. Plausible models that do generate temporal symmetry, from both thalamic and intracortical inputs, are presented. In particular, we propose that two specific characteristics of the thalamocortical interface may be responsible. The first is a temporal mismatch between the fast dynamics of the thalamus and the slow responses of the cortex. The second is that all thalamic inputs into a cortical module (or a cluster of cells) must be restricted to one point of entry (or one cell in the cluster). This latter property implies a lack of correlated horizontal interactions across cortical modules during the STRF measurements. The implications of these insights in the auditory system, and comparisons with similar properties in the visual system, are explored.

Using evoked potentials to match interaural electrode pairs with bilateral cochlear implants

Bilateral cochlear implantation seeks to restore the advantages of binaural hearing to the profoundly deaf by providing binaural cues normally important for accurate sound localization and speech reception in noise. Psychophysical observations suggest that a key issue for the implementation of a successful binaural prosthesis is the ability to match the cochlear positions of stimulation channels in each ear. We used a cat model of bilateral cochlear implants with eight-electrode arrays implanted in each cochlea to develop and test a noninvasive method based on evoked potentials for matching interaural electrodes. The arrays allowed the cochlear location of stimulation to be independently varied in each ear. The binaural interaction component (BIC) of the electrically evoked auditory brainstem response (EABR) was used as an assay of binaural processing. BIC amplitude peaked for interaural electrode pairs at the same relative cochlear position and dropped with increasing cochlear separation in either direction. To test the hypothesis that BIC amplitude peaks when electrodes from the two sides activate maximally overlapping neural populations, we measured multiunit neural activity along the tonotopic gradient of the inferior colliculus (IC) with 16-channel recording probes and determined the spatial pattern of IC activation for each stimulating electrode. We found that the interaural electrode pairings that produced the best aligned IC activation patterns were also those that yielded maximum BIC amplitude. These results suggest that EABR measurements may provide a method for assigning frequency-channel mappings in bilateral implant recipients, such as pediatric patients, for which psychophysical measures of pitch ranking or binaural fusion are unavailable.

The contribution of spike threshold to acoustic feature selectivity, spike information content, and information throughput

Hypotheses of sensory coding range from the notion of nonlinear "feature detectors" to linear rate coding strategies. Here, we report that auditory neurons exhibit a novel trade-off in the relationship between sound selectivity and the information that can be communicated to a postsynaptic cell. Recordings from the cat inferior colliculus show that neurons with the lowest spike rates reliably signal the occurrence of stereotyped stimulus features, whereas those with high response rates exhibit lower selectivity. The highest information conveyed by individual action potentials comes from neurons with low spike rate and high selectivity. Surprisingly, spike information is inversely related to spike rates, following a trend similar to that of feature selectivity. Information per time interval, however, was proportional to measured spike rates. A neuronal model based on the spike threshold of the synaptic drive accurately accounts for this trade-off: higher thresholds enhance the spiking fidelity at the expense of limiting the total communicated information. Such a constraint on the specificity and throughput creates a continuum in the neural code with two extreme forms of information transfer that likely serve complementary roles in the representation of the auditory environment.

Nonlinear Modeling of Auditory-Nerve Rate Responses to Wideband Stimuli

The spectral selectivity of auditory nerve fibers was characterized by a method based on responses to random-spectrum-shape stimuli. The method models the average discharge rate of fibers for steady stimuli and is based on responses to ≈100 noise-like stimuli with pseudorandom spectral levels in 1/8- or 1/16-octave frequency bins. The model assumes that rate is determined by a linear weighting of the spectrum plus a second-order weighting of all pairs of spectrum values within a certain frequency range of best frequency. The method allows prediction of rate responses to stimuli with arbitrary wideband spectral shapes, thus providing a direct test of the degree of linearity of spectral processing Auditory-nerve fibers are shown to rely mainly on linear weighting of the stimulus spectrum; however, significant second-order terms are present and are important in predicting responses to random-spectrum shape stimuli, although not for predicting responses to noise filtered with cat head-related transfer functions. The second-order terms weight the products of levels at identical frequencies positively and the products of different frequencies negatively. As such, they model both curvature in the rate versus level function and suppressive interactions between different frequency components. The first- and second-order characterizations derived in this method provide a measure of higher-order nonlinearities in neurons, albeit without providing information about temporal characteristics.

Adaptive stimulus optimization for auditory cortical neurons

Despite the extensive physiological work performed on auditory cortex, our understanding of the basic functional properties of auditory cortical neurons is incomplete. For example, it remains unclear what stimulus features are most important for these cells. Determining these features is challenging given the considerable size of the relevant stimulus parameter space as well as the unpredictable nature of many neurons' responses to complex stimuli due to nonlinear integration across frequency. Here we used an adaptive stimulus optimization technique to obtain the preferred spectral input for neurons in macaque primary auditory cortex (AI). This method uses a neuron's response to progressively modify the frequency composition of a stimulus to determine the preferred spectrum. This technique has the advantage of being able to incorporate nonlinear stimulus interactions into a "best estimate" of a neuron's preferred spectrum. The resulting spectra displayed a consistent, relatively simple circumscribed form that was similar across scale and frequency in which excitation and inhibition appeared about equally prominent. In most cases, this structure could be described using two simple models, the Gabor and difference of Gaussians functions. The findings indicate that AI neurons are well suited for extracting important scale-invariant features in sound spectra and suggest that they are designed to efficiently represent natural sounds.

Delayed inhibition in cortical receptive fields and the discrimination of complex stimuli

Although auditory cortex is thought to play an important role in processing complex natural sounds such as speech and animal vocalizations, the specific functional roles of cortical receptive fields (RFs) remain unclear. Here, we study the relationship between a behaviorally important function: the discrimination of natural sounds and the structure of cortical RFs. We examine this problem in the model system of songbirds, using a computational approach. First, we constructed model neurons based on the spectral temporal RF (STRF), a widely used description of auditory cortical RFs. We focused on delayed inhibitory STRFs, a class of STRFs experimentally observed in primary auditory cortex (ACx) and its analog in songbirds (field L), which consist of an excitatory subregion and a delayed inhibitory subregion cotuned to a characteristic frequency. We quantified the discrimination of birdsongs by model neurons, examining both the dynamics and temporal resolution of discrimination, using a recently proposed spike distance metric (SDM). We found that single model neurons with delayed inhibitory STRFs can discriminate accurately between songs. Discrimination improves dramatically when the temporal structure of the neural response at fine timescales is considered. When we compared discrimination by model neurons with and without the inhibitory subregion, we found that the presence of the inhibitory subregion can improve discrimination. Finally, we modeled a cortical microcircuit with delayed synaptic inhibition, a candidate mechanism underlying delayed inhibitory STRFs, and showed that blocking inhibition in this model circuit degrades discrimination.

Naturalistic auditory contrast improves spectrotemporal coding in the cat inferior colliculus

Statistical analysis of natural sounds and speech reveals logarithmically distributed spectrotemporal modulations that can cover several orders of magnitude. By contrast, most artificial stimuli used to probe auditory function, including pure tones and white noise, have linearly distributed amplitude fluctuations with a limited average dynamic range. Here we explore whether the operating range of the auditory system is physically matched to the statistical structure of natural sounds. We recorded single-unit and multi-unit neuronal activity from the central nucleus of the cat inferior colliculus (ICC) in response to dynamic spectrotemporal sound sequences to determine whether ICC neurons respond preferentially to linear or logarithmic spectrotemporal amplitudes. We varied the intensity, dynamic range, and contrast statistics of these sounds to mimic those of natural and artificial stimuli. ICC neurons exhibited monotonic and nonmonotonic contrast dependencies with increasing dynamic range that were independent of the stimulus intensity. Midbrain neurons had higher firing rates and higher receptive field energies and showed a net improvement in spectrotemporal encoding ability for logarithmic stimuli, with an increase in the mutual information rate of approximately 50% over linear amplitude sounds. This efficient use of logarithmic spectrotemporal modulations by auditory midbrain neurons reflects a neural adaptation to structural regularities in natural sounds and likely underlies human perceptual abilities.