Dataset Information

Brain-optimized extraction of complex sound features that drive continuous auditory perception.

ABSTRACT: Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match the neural representation of sound. Here, we postulate that constructing a data-driven neural model of auditory perception, with a minimum of theoretical assumptions about the relevant sound features, could provide an alternative approach and possibly a better match to the neural responses. We collected electrocorticography recordings from six patients who watched a long-duration feature film. The raw movie soundtrack was used to train an artificial neural network model to predict the associated neural responses. The model achieved high prediction accuracy and generalized well to a second dataset, where new participants watched a different film. The extracted bottom-up features captured acoustic properties that were specific to the type of sound and were associated with various response latency profiles and distinct cortical distributions. Specifically, several features encoded speech-related acoustic properties with some features exhibiting shorter latency profiles (associated with responses in posterior perisylvian cortex) and others exhibiting longer latency profiles (associated with responses in anterior perisylvian cortex). Our results support and extend the current view on speech perception by demonstrating the presence of temporal hierarchies in the perisylvian cortex and involvement of cortical sites outside of this region during audiovisual speech perception.

SUBMITTER: Berezutskaya J

PROVIDER: S-EPMC7363106 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Brain-optimized extraction of complex sound features that drive continuous auditory perception.

Berezutskaya Julia J Freudenburg Zachary V ZV Güçlü Umut U van Gerven Marcel A J MAJ Ramsey Nick F NF

PLoS computational biology 20200702 7

Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match the neural representation of sound. Here, we postulate that constructing a data-driven neural model of auditory perception, with a minimum of theoretical assumptions about the relevant sound features, could provide an alternative approach and possibl ...[more]

PMID: 32614826

Similar Datasets

Project description:The ability to process and act upon incoming sounds during locomotion is critical for survival and adaptive behavior. Despite the established role that the auditory cortex (AC) plays in behavior- and context-dependent sound processing, previous studies have found that auditory cortical activity is on average suppressed during locomotion as compared to immobility. While suppression of auditory cortical responses to self-generated sounds results from corollary discharge, which weakens responses to predictable sounds, the functional role of weaker responses to unpredictable external sounds during locomotion remains unclear. In particular, whether suppression of external sound-evoked responses during locomotion reflects reduced involvement of the AC in sound processing or whether it results from masking by an alternative neural computation in this state remains unresolved. Here, we tested the hypothesis that rather than simple inhibition, reduced sound-evoked responses during locomotion reflect a tradeoff with the emergence of explicit and reliable coding of locomotion velocity. To test this hypothesis, we first used neural inactivation in behaving mice and found that the AC plays a critical role in sound-guided behavior during locomotion. To investigate the nature of this processing, we used two-photon calcium imaging of local excitatory auditory cortical neural populations in awake mice. We found that locomotion had diverse influences on activity of different neurons, with a net suppression of baseline-subtracted sound-evoked responses and neural stimulus detection, consistent with previous studies. Importantly, we found that the net inhibitory effect of locomotion on baseline-subtracted sound-evoked responses was strongly shaped by elevated ongoing activity that compressed the response dynamic range, and that rather than reflecting enhanced "noise," this ongoing activity reliably encoded the animal's locomotion speed. Decoding analyses revealed that locomotion speed and sound are robustly co-encoded by auditory cortical ensemble activity. Finally, we found consistent patterns of joint coding of sound and locomotion speed in electrophysiologically recorded activity in freely moving rats. Together, our data suggest that rather than being suppressed by locomotion, auditory cortical ensembles explicitly encode it alongside sound information to support sound perception during locomotion.

Project description:Previous studies on the effect of spectral content on auditory distance perception (ADP) focused on the physically measurable cues occurring either in the near field (low-pass filtering due to head diffraction) or when the sound travels distances >15 m (high-frequency energy losses due to air absorption). Here, we study how the spectrum of a sound arriving from a source located in a reverberant room at intermediate distances (1-6 m) influences the perception of the distance to the source. First, we conducted an ADP experiment using pure tones (the simplest possible spectrum) of frequencies 0.5, 1, 2, and 4 kHz. Then, we performed a second ADP experiment with stimuli consisting of continuous broadband and bandpass-filtered (with center frequencies of 0.5, 1.5, and 4 kHz and bandwidths of 1/12, 1/3, and 1.5 octave) pink-noise clips. Our results showed an effect of the stimulus frequency on the perceived distance both for pure tones and filtered noise bands: ADP was less accurate for stimuli containing energy only in the low-frequency range. Analysis of the frequency response of the room showed that the low accuracy observed for low-frequency stimuli can be explained by the presence of sparse modal resonances in the low-frequency region of the spectrum, which induced a non-monotonic relationship between binaural intensity and source distance. The results obtained in the second experiment suggest that ADP can also be affected by stimulus bandwidth but in a less straightforward way (i.e., depending on the center frequency, increasing stimulus bandwidth could have different effects). Finally, the analysis of the acoustical cues suggests that listeners judged source distance using mainly changes in the overall intensity of the auditory stimulus with distance rather than the direct-to-reverberant energy ratio, even for low-frequency noise bands (which typically induce high amount of reverberation). The results obtained in this study show that, depending on the spectrum of the auditory stimulus, reverberation can degrade ADP rather than improve it.

Dataset Information

Brain-optimized extraction of complex sound features that drive continuous auditory perception.

Publications

Brain-optimized extraction of complex sound features that drive continuous auditory perception.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets