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Neural modulation tuning characteristics scale to efficiently encode natural sound statistics.

ABSTRACT: The efficient-coding hypothesis asserts that neural and perceptual sensitivity evolved to faithfully represent biologically relevant sensory signals. Here we characterized the spectrotemporal modulation statistics of several natural sound ensembles and examined how neurons encode these statistics in the central nucleus of the inferior colliculus (CNIC) of cats. We report that modulation-tuning in the CNIC is matched to equalize the modulation power of natural sounds. Specifically, natural sounds exhibited a tradeoff between spectral and temporal modulations, which manifests as 1/f modulation power spectrum (MPS). Neural tuning was highly overlapped with the natural sound MPS and neurons approximated proportional resolution filters where modulation bandwidths scaled with characteristic modulation frequencies, a behavior previously described in human psychoacoustics. We demonstrate that this neural scaling opposes the 1/f scaling of natural sounds and enhances the natural sound representation by equalizing their MPS. Modulation tuning in the CNIC may thus have evolved to represent natural sound modulations in a manner consistent with efficiency principles and the resulting characteristics likely underlie perceptual resolution.

SUBMITTER: Rodriguez FA

PROVIDER: S-EPMC3351116 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Neural modulation tuning characteristics scale to efficiently encode natural sound statistics.

Rodríguez Francisco A FA Chen Chen C Read Heather L HL Escabí Monty A MA

The Journal of neuroscience : the official journal of the Society for Neuroscience 20101101 47

The efficient-coding hypothesis asserts that neural and perceptual sensitivity evolved to faithfully represent biologically relevant sensory signals. Here we characterized the spectrotemporal modulation statistics of several natural sound ensembles and examined how neurons encode these statistics in the central nucleus of the inferior colliculus (CNIC) of cats. We report that modulation-tuning in the CNIC is matched to equalize the modulation power of natural sounds. Specifically, natural sounds ...[more]

PMID: 21106835

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Project description:Natural sounds contain rich patterns of amplitude modulation (AM), which is one of the essential sound dimensions for auditory perception. The sensitivity of human hearing to AM measured by psychophysics takes diverse forms depending on the experimental conditions. Here, we address with a single framework the questions of why such patterns of AM sensitivity have emerged in the human auditory system and how they are realized by our neural mechanisms. Assuming that optimization for natural sound recognition has taken place during human evolution and development, we examined its effect on the formation of AM sensitivity by optimizing a computational model, specifically, a multilayer neural network, for natural sound (namely, everyday sounds and speech sounds) recognition and simulating psychophysical experiments in which the AM sensitivity of the model was assessed. Relatively higher layers in the model optimized to sounds with natural AM statistics exhibited AM sensitivity similar to that of humans, although the model was not designed to reproduce human-like AM sensitivity. Moreover, simulated neurophysiological experiments on the model revealed a correspondence between the model layers and the auditory brain regions. The layers in which human-like psychophysical AM sensitivity emerged exhibited substantial neurophysiological similarity with the auditory midbrain and higher regions. These results suggest that human behavioral AM sensitivity has emerged as a result of optimization for natural sound recognition in the course of our evolution and/or development and that it is based on a stimulus representation encoded in the neural firing rates in the auditory midbrain and higher regions.SIGNIFICANCE STATEMENT This study provides a computational paradigm to bridge the gap between the behavioral properties of human sensory systems as measured in psychophysics and neural representations as measured in nonhuman neurophysiology. This was accomplished by combining the knowledge and techniques in psychophysics, neurophysiology, and machine learning. As a specific target modality, we focused on the auditory sensitivity to sound AM. We built an artificial neural network model that performs natural sound recognition and simulated psychophysical and neurophysiological experiments in the model. Quantitative comparison of a machine learning model with human and nonhuman data made it possible to integrate the knowledge of behavioral AM sensitivity and neural AM tunings from the perspective of optimization to natural sound recognition.

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Neural modulation tuning characteristics scale to efficiently encode natural sound statistics.

Publications

Neural modulation tuning characteristics scale to efficiently encode natural sound statistics.

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