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Title: Constructing Noise-Invariant Representations of Sound in the Auditory Pathway}
Article:
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.
Introduction:
Because our auditory world usually contains many competing sources, behaviorally important sounds are often obscured by background noise. To accurately recognize these sounds, the auditory brain must therefore represent them in a way that is robust to noise. Previous work has suggested that the auditory system does build such sound representations. In the auditory periphery, sounds are represented in terms of their physical structure, including any noise[1]–[3], while data from human imaging studies suggest that, in higher areas of auditory cortex( AC), relevant sounds are represented in a more context-independent, categorical manner[4]–[8]. However, we know very little about the neural computations that might generate noise invariance or where exactly along the auditory pathway this is achieved. We do, on the other hand, know that the firing patterns of individual auditory neurons change with acoustic context. Numerous experiments have varied the statistics of sound stimulation, such as sounds' overall intensity, modulation depth, or contrast, or the presence of background noise. In response to these manipulations, auditory neurons from the periphery to primary cortex have been observed to change their gain[9]–[12], temporal receptive field shape( i. e., modulation transfer function, MTF)[9],[11],[13]–[17], spectral receptive field shape[18],[19], and output nonlinearities[20],[21], or they undergo more complex changes in response patterns[22],[23]. These changes have been explored or explained in terms of signal detection theory[11], efficient coding[17],[20],[24], or maintaining sensitivity to ecologically relevant stimuli[21],[23]. Such forms of adaptation—not to the repetition of a fixed stimulus, but to the statistics of ongoing stimulation—offer a plausible neural mechanism for the construction of noise-invariant representations. A population of neurons that adapts to the constant statistics of a background noise could become desensitized to that noise, while still accurately representing simultaneously presented, modulated foreground sounds. Here, we investigated whether adaptation to stimulus statistics in the auditory system enables the brain to build noise-invariant representations of sounds. To do this, we carried out three experiments. First, we measured neural responses to complex sounds embedded in stationary noise, by recording from single units and small multi-unit clusters in the auditory midbrain and cortex and by simulating responses in the auditory periphery. We find that as one progresses through the auditory pathway, neural responses become progressively more independent of the level of background noise. Second, we measured how the coding of individual neurons in these auditory centers is affected by the changes in stimulus statistics induced by adding background noise. We find that there is a progressive increase through the auditory pathway in the strength of adaptation to the altered stimulus statistics. Third, we considered how the noise-dependent responses of individual units combine to produce population codes. Population representations are usually addressed only indirectly, for example, by summing up results from individual units( though see[25],[26]), but here we investigated these directly, by asking how well the original, “clean” sounds could be decoded from the population responses to noise-tainted stimuli. We find a progressive increase in the noise tolerance of population representations of sound. Moreover, neuron-level changes in the strength of adaptation and population-level changes in the noise tolerance of decoding are well correlated both within and across auditory centers. This suggests that adaptation to stimulus statistics may indeed be a neural mechanism that drives the construction of noise-tolerant representations of sound.
Discussion:
The effect of- and-adaptation can be understood by representing the structure of a sound as a time-varying function,. The brain does not have direct access to; instead, when the sound is produced at a particular amplitude() and is heard against a background of other sounds(), the signal that the ear actually receives is the sound mixture. To identify a sound, the brain must recover the sound structure,, without being confused by the often irrelevant variables and. Experiments with synthetic DRC stimuli show a shift in coding away from a raw signal( resembling) in the periphery toward a more normalized signal( resembling) in the cortex. When the contrast of DRCs is manipulated, we find that sAN responses to DRCs are reasonably well described by an LN model without gain changes. Their firing rate is a function of—that is, the DRC filtered through that neuron's STRF( Figure 5B). This suggests that the AN, as a whole, provides a relatively veridical representation of sound mixtures reaching the ear. In comparison, many cortical units, and some IC units, adapt to changes in DRC contrast by changing their gain. These units' firing rates are not a function of( as in the sAN); they are often better described as a function of a normalized variable,, in which the stimulus contrast() has been divided out( Figure 5C). Even though AC neurons do not show complete contrast-invariance for these stimuli( the median AC gain change was 44%; perfect-encoding would be 100% gain change; Figure 5D), AC neurons' responses depend less on stimulus contrast than those in IC or sAN. A similar shift in coding is evident when considering small changes in the mean level of a DRC. Whereas each sAN fiber provides a relatively fixed representation of, IC and AC units adjust their baseline firing rates so that they effectively subtract out the stimulus mean( Figures S7 and S8). The effect of adaptation to stimulus statistics is thus that cortex( and, to a lesser degree, IC) provides a sound representation that is closer to the underlying sound,, than to the sound mixture reaching the ear,. It is likely that adaptation to stimulus statistics is one of several changes in neural coding that contributes towards the construction of noise-invariant representations of sounds. Related findings were obtained by Lesica and Grothe[17], who studied changes in MTFs of IC neurons under noisy stimulation. Just as our investigation of- and-adaptations was initially motivated by considering how the statistics of within-receptive field signals would change under clean and noisy sound stimulation( Figure 3), so Lesica and Grothe began by investigating the difference in the amplitude modulation spectra between foreground vocalizations and background noises. They observed that vocalizations contain more power in slow( Hz) amplitude modulations than background noises. When the authors presented vocalizations to gerbils and recorded from neurons in the IC, they found that single units' MTFs shifted from being bandpass to more lowpass, suggesting that IC neurons redirect their coding capacity to modulation bands of higher SNR under noisy conditions. Similar results were recently obtained by Ding and Simon[8], who measured the aggregate activity in human AC via magnetoencephalography, as subjects listened to speech in spectrally matched noise. They found that as background noise is added to speech, the entrainment of aggregate cortical activity to slow temporal modulations( <4 Hz) in the speech signal remains high, while entrainment to faster( 4–8 Hz) modulations degrades with noise. Since the gross envelope of the original speech can be decoded from aggregate responses to the clean and noisy stimuli, noise induces a change in response gain as well as changes to MTFs. The relationship between our observations of increasing-adaptation from periphery to cortex, and these previous findings of changing MTFs in IC neurons and aggregate cortical activity, may depend on the modulation specificity of the gain changes. For instance, a nonspecific increase in neural response gain would manifest as an overall upwards shift in the MTF. Conversely, an upwards shift within a small region of the MTF corresponds to a modulation-band–specific increase in gain. One possibility is that during complex sound stimulation, auditory neurons determine their gain independently for different modulation “channels”( such as described in modulation filterbank models[28],[46]), as a function of the signal statistics within each channel. This might have different effects on MTFs depending on the modulation spectrum of the background noise. In indirect support of this possibility, the extent to which the coding of different cells is affected by a given background noise appears to depend on each cell's modulation tuning[47]. An alternative possibility is that auditory neurons might always become more modulation lowpass in the presence of background noise, regardless of the noise's actual modulation statistics. This might reflect a set of priors about what is signal and what is noise in an incoming sound mixture. Our set of unique sounds and background noises was too small to test these two hypotheses( or even to measure MTFs). Nevertheless, if auditory neurons additionally demonstrate modulation-specific gain in response to noise, it is likely that this effect grows stronger from periphery to cortex. These data also provide some insight as to how our results might extend to more complex classes of background noise. Here, we have characterized coding changes induced by adding stationary noise with flat modulation spectra and no cross-band correlations. Many background sounds have more complex( often 1/f-like) modulation spectra[28],[35]; a greater proportion of their modulation energy lies within the common passband of midbrain and cortical auditory neurons. Since our simulations suggest that greater modulation tuning plays only a small part in enabling tolerance to noise with flat modulation spectra, it should be less important still for enabling tolerance to noise with 1/f-like modulation spectra. We therefore expect that the adaptive coding we and others describe is crucial for more general classes of background noise. Beyond this, some background sound textures also contain correlations across carrier or modulation channels[28], while others are nonstationary, changing their statistics over time. An understanding of how these noise features differentially affect signal encodings along the auditory pathway would require further experiments utilizing a broader set of background noises. An alternative hypothesis for how the brain builds noise-invariant representations of sound is that the very nature of these representations may be changing along the auditory pathway, from an emphasis on encoding predominantly spectrotemporal information in the periphery to encoding information about the presence of higher level auditory features in cortex. This, for instance, is a position recently argued for by Chechik and Nelken[48], based on their investigation of the responses of cat cortical neurons to the components of natural birdsong. Emerging data from the avian brain support this idea: the avian analogue of AC appears to shift its encoding toward sparse representations of song elements, which can be encoded in a noise-robust manner[49]. Our results relate to this hypothesis by emphasizing that, to the extent that the mammalian midbrain and cortex do encode spectrotemporal information about ongoing sounds, they do so in progressively more normalized coordinates. This captures at least some( but likely not all) of the proposed representational shifts from periphery to cortex. Finally, bottom-up mechanisms are undoubtedly just a part of a broader infrastructure for selecting and enhancing representations of particular sounds heard within complex acoustic scenes. In our experiments, we chose stimuli for which the assignment of the tags “signal” and “noise”( or “foreground”/“background, ” or “relevant”/“irrelevant”) to components of the mixture is reasonably justified by the different statistical structures of natural and background sounds[17],[28],[35],[50]. On the other hand, there are also many real-world situations for which such assignment is ambiguous, and depends on task-specific demands. Listening to a single talker against a background of many is one notable instance. Yet human imaging studies reveal that in such circumstances, the neural representation of attended talkers is selectively enhanced relative to that of unattended talkers, even at low SNRs[7],[26],[51]. While noise tolerance appears to grow even stronger between core and belt AC[7],[8], this is likely to be attention-dependent[7],[8],[52]–[54]. Understanding how we create noise-tolerant representations of sound within more complex mixtures is thus interwoven with questions of how we segment these scenes, how we tag the components as “signal” and “noise, ” and how we direct our attention accordingly. In sum, our results provide a clear picture of a bottom-up process that contributes to the emergence of noise-invariant representations of natural sounds in the auditory brain. As neurons' adaptation to stimulus statistics gradually grows stronger along the auditory pathway, populations of these neurons progressively shift from encoding low-level physical attributes of incoming sounds towards more mean-, contrast-, and noise-independent information about stimulus identity. The result is a major computational step towards the context-invariant, categorical sound representations that are seen in higher areas of AC.
We rarely hear sounds ( such as someone talking ) in isolation , but rather against a background of noise . When mixtures of sounds and background noise reach the ears , peripheral auditory neurons represent the whole sound mixture . Previous evidence suggests , however , that the higher auditory brain represents just the sounds of interest , and is less affected by the presence of background noise . The neural mechanisms underlying this transformation are poorly understood . Here , we investigate these mechanisms by studying the representation of sound by populations of neurons at three stages along the auditory pathway; we simulate the auditory nerve and record from neurons in the midbrain and primary auditory cortex of anesthetized ferrets . We find that the transformation from noise-sensitive representations of sound to noise-tolerant processing takes place gradually along the pathway from auditory nerve to midbrain to cortex . Our results suggest that this results from neurons adapting to the statistics of heard sounds .