Constructing noise-invariant representations of sound in the auditory pathway

Abstract

Identifying behaviorally relevant sounds in the presence of background noise is one of the most important and poorly understood challenges faced by the auditory system. An elegant solution to this problem would be for the auditory system to represent sounds in a noise-invariant fashion. Since a major effect of background noise is to alter the statistics of the sounds reaching the ear, noise-invariant representations could be promoted by neurons adapting to stimulus statistics. Here we investigated the extent of neuronal adaptation to the mean and contrast of auditory stimulation as one ascends the auditory pathway. We measured these forms of adaptation by presenting complex synthetic and natural sounds, recording neuronal responses in the inferior colliculus and primary fields of the auditory cortex of anaesthetized ferrets, and comparing these responses with a sophisticated model of the auditory nerve. We find that the strength of both forms of adaptation increases as one ascends the auditory pathway. To investigate whether this adaptation to stimulus statistics contributes to the construction of noise-invariant sound representations, we also presented complex, natural sounds embedded in stationary noise, and used a decoding approach to assess the noise tolerance of the neuronal population code. We find that the code for complex sounds in the periphery is affected more by the addition of noise than the cortical code. We also find that noise tolerance is correlated with adaptation to stimulus statistics, so that populations that show the strongest adaptation to stimulus statistics are also the most noise-tolerant. This suggests that the increase in adaptation to sound statistics from auditory nerve to midbrain to cortex is an important stage in the construction of noise-invariant sound representations in the higher auditory brain.

Figure 1. Single unit responses to clean and noisy sounds.

Left column, the spectrogram of a segment of speech under four noise conditions, with the noise level increasing (i.e., the SNR decreasing) from top to bottom. Second to fourth columns, example rasters showing the responses of sAN responses and of responses recorded in the IC and AC, over 50 stimulus presentations. Gray lines, average PSTH.

Figure 2. Along the auditory pathway, neurons' response distributions become increasingly independent of the level of background noise.

(A) Average distribution of normalized firing rates by location/SNR. For each unit, , where is the firing rate. This shows that the average response distribution within the population changes less with noise in higher auditory centers. (B) Kullback–Leibler divergence between individual units' normalized firing-rate distributions evoked from clean sounds and evoked from noisy sounds. Smaller values indicate that firing rate distributions were similar. This shows that individual neurons' response distributions change less with noise in higher auditory centers. (C) Statistical independence of stimulus-conditioned response distributions to the background noise level (see Materials and Methods for details of metric). Lower values indicate that response distributions were highly dependent on the stimulus SNR; a value of 1 indicates that response distributions were completely independent of the stimulus SNR. Median values of 0.80/0.84/0.88 for sAN/IC/AC (, pairwise rank-sums tests).

Figure 3. Effect of background noise on incoming signals within neurons' receptive fields.

(A) Left, sound intensity within a cortical neuron's receptive field for clean (20 dB) and noisy (0 dB) stimulation (see Figure S1B). Right, distribution of the sounds' within-channel intensities. (B) Signals in (A) after adaptation to signal statistics.

Figure 4. Increasing adaptation to stimulus baseline along the auditory pathway.

(A) Calculation of BI, a measure of -adaptation, for an example sAN fiber. CDF, cumulative distribution of firing rates. , the 33rd percentile of the CDF under clean sound stimulation —that is, the firing rate with the cumulative probability . BI indicates how little changes with SNR, as . (B) Units' BI in each location.

Figure 5. Increasing adaptation to stimulus contrast along the auditory pathway.

(A) Schematic of adaptive-LN model. Top/bottom, DRC stimuli. DRCs are filtered through a STRF, then passed through an output nonlinearity, yielding the firing rate (). Output nonlinearities change with stimulus contrast. Insets, example time series. (B) Example units, nonlinearities during low (blue) and high (red) contrast DRCs. Insets, STRFs. Bottom, distributions of STRF-filtered DRCs under low/high contrast. (C) Nonlinearities in (B), replotted in normalized coordinates. (D) Contrast-dependent changes to the slope of units' nonlinearities. (E) Percentage of residual signal power explained by gain kernel model above an LN model . (F) Log increase in Fisher information in units' encoding of low contrast stimuli, resulting from adaptation to this distribution. Zero, no adaptation. Larger positive values, greater adaptation.

Figure 6. Decoding the population representations of clean and noisy sounds.

Schematic of the decoding of neural responses. For each auditory center, a decoder was trained to reconstruct the clean sound spectrogram from the population responses to the clean sounds. We then measured the performance of these decoders when reconstructing spectrograms from the responses to both clean and noisy sounds. Top row, spectrogram of a 2(20 dB SNR) and noisy (10/0/−10 dB SNR) conditions. Left column, decoder training from responses to clean sounds. Population responses are shown as neurograms: each row depicts the time-varying firing rate of a single unit in the population; rows are organized by CF. Right, reconstructed spectrograms () from population responses to noisy sounds, using the same decoders as trained on the left. The similarity between the reconstructed spectrogram and the presented spectrogram is measured by ; likewise, the similarity between and the original, clean spectrogram is measured by . The tendencies for the sAN decoder to produce -like spectrograms, and the IC and AC decoders to produce -like spectrograms, are most visible for the 0 dB and −10 dB conditions.

Figure 7. Population representations of natural sounds become more noise-tolerant along the auditory pathway.

(A) Similarity between decoded responses to the clean sounds (), and the clean sounds' spectrograms (). Abscissa, sampled population size. Colored areas, bootstrapped 95% confidence intervals. (B–C) Similarity between decoded responses to the noisy sounds (), and the spectrograms of the presented, noisy sounds (B), or the spectrograms of the original, clean sounds (C). Reconstructions are from the full populations in each location. Red bars are the same in (B) and (C), denoting (i.e., the rightmost points for each curve in A). Error bars, bootstrapped 95% confidence intervals. (D) Index of whether decoded responses were more similar to the presented, noisy sound (negative values), or the original, clean sound (positive values). Similarities denoted by asterisks () are normalized to the maximum score for each location, . Error bars, 95% confidence intervals. Pairwise comparison statistics (bootstrapped): (***), (**), (*). (E) Decoder accuracy in recovering the clean sound's identity from noisy responses, relative to accuracy in doing so from clean responses.

Figure 8. Higher - and -adaptation explain the increased noise-tolerance of population representations.

(A) Relationship between decoder performance and BI (measure of -adaptation). Each point represents a subpopulation (one quarter) of the units from each of the sAN/IC/AC populations, subdivided according to units' BI (values in Figure 4B). Abscissa, mean BI in the subpopulation. Ordinate, performance of the subpopulation decoder. Lines, linear fit per SNR. (B) Relationship between decoder performance and CI (measure of -adaptation), similar to (A). Here, each point represents a subpopulation (one quarter) of the units from each of the sAN/IC/AC populations, subdivided according to the amount of units' contrast adaptation (values in Figure 5D). sAN values of were adjusted for low BI (see Figure S6).