Receptive Fields Of Auditory Neurons Research Articles

Contour representation of sound signals

Event Abstract Back to Event Contour representation of sound signals Yoonseob Lim1*, Barbara G. Shinn-Cunningham1 and Timothy Gardner2 1 Boston University, Department of Cognitive and Neural Systems, United States 2 Boston University, Faculty of Electrical Engineering and Computing, United States Continuous edges, or contours, are powerful features for object recognition, both in neural and machine vision. Similarly, auditory signals are characterized by sharp edges in some classes of time-frequency analysis. Linking these edges to form contours could be relevant for auditory signal processing. However, the mathematical foundations of a general contour representation of sound have not been established. Sinusoidal representations of voiced speech and music have been explored, but these approaches do not represent broadband signals efficiently. Here we construct a two-dimensional contour representation that is generally applicable to any time series, including sound. Starting with the Short Time Fourier Transform (STFT), the method defines edges by coherent phase structure at local points in the time-frequency plane (zero crossings of a complex reassignment matrix). Continuous edges are grouped to form contours that follow the ridges and valleys of the traditional STFT. Local amplitudes are assigned by calculation of fixed points in an iterated reassignment mapping. The representation is additive; the complex amplitudes of the contours can be directly summed to reproduce the original signal. This re-synthesis matches the original signal with a signal-to-noise ratio of 15 dB or higher, even in the challenging case of white noise. In practice, this level of precision provides perceptually equivalent representations of speech and music. For many sounds of interest, a subset of the full contour collection can provide an accurate representation. To find this compact subset, an over-complete set of contours are calculated using multiple filter bandwidths. Contours are then ranked by power, length and curvature, and subject to lateral inhibition from neighboring contours. The top-ranking contours in this distribution provide a sparse representation that emerges without any prior suppositions about the nature of the original signal. By combining contours from multiple bandwidths, the representation achieves high precision in both time and frequency. As such, the method is relevant to a wide range of time-frequency tasks such as constructing receptive fields of auditory neurons, characterizing animal vocalizations, pattern recognition and signal de-noising. We speculate that neural auditory processing involves a similar contour representation. Each stage in the analysis is a plausible operation for neurons: parallel and redundant primary processing in multiple bandwidths, grouping by phase coherence, linking by continuity and lateral inhibition. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session I Citation: Lim Y, Shinn-Cunningham BG and Gardner T (2010). Contour representation of sound signals. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00133 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 01 Mar 2010; Published Online: 01 Mar 2010. * Correspondence: Yoonseob Lim, Boston University, Department of Cognitive and Neural Systems, Boston, United States, yslim@bu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yoonseob Lim Barbara G Shinn-Cunningham Timothy Gardner Google Yoonseob Lim Barbara G Shinn-Cunningham Timothy Gardner Google Scholar Yoonseob Lim Barbara G Shinn-Cunningham Timothy Gardner PubMed Yoonseob Lim Barbara G Shinn-Cunningham Timothy Gardner Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Coding of sound features in primary and secondary auditory areas of songbirds.

We have used a combination of neuroethological and classical auditory neurophysiological approaches to study how behaviorally relevant sounds are processed in the avian auditory system. One of the contributions of the neuroethological approach has been the discovery of highly specific auditory neurons that appear to be specialized for detecting very specific behaviorally relevant sounds. On the other hand, many auditory neurons recorded in the auditory system of nonspecialists do not exhibit such specificity. At the same time, animals and humans hear and process a large space of sounds and are able to categorize these in much broader perceptual terms, describing them in terms of their pitch, timbre, and rhythm. By systematic analyzing neural responses to song in the ascending avian auditory system and relating receptive fields to the statistics of natural sounds, we have shown that these two approaches can be unified: we found that the spectrotemporal receptive fields of auditory neurons tile a subset of the acoustical space that is particular important for natural sounds. In addition, we found that neurons could be classified into functional clusters. Neurons in different clusters were sensitive to different song features and, we will argue, are involved in mediating distinct perceptual attributes.

Noise changes receptive fields of auditory neurons in A1

Primates engage in auditory behaviors under a broad range of signal to noise conditions. In this study, optimal linear receptive fields were measured in alert primate A1 in response to stimuli that vary in spectrotemporal density. As the random tone pip density increased, selective A1 excitatory receptive fields systematically changed. Receptive field sensitivity, expressed as the expected change in firing rate after a tone pip onset, decreased by an order of magnitude. Spectral selectivity more than doubled. Inhibitory subfields, which were rarely recorded at low sound densities, emerged at higher sound densities. The ratio of excitatory to inhibitory population strength changed from 14.4:1 to 1.4:1. At low sound densities, the sound associated with the evocation of an action potential from an A1 neuron was broad in spectrum and time. At high sound densities, a spike-evoking sound was more likely to be a spectral or temporal edge, and was narrower in both time and frequency range. Prediction experiments were performed to validate the assumption that linear receptive fields were representative of neural responses at high noise densities. The auditory context alters A1 responses across multiple parameter spaces; this presents a challenge for reconstructing neural codes.

A rapid correlation method for the analysis of spectro-temporal receptive fields of auditory neurons

The evaluation of spectro-temporal receptive fields in auditory neurons makes use of a correlation technique that needs a high amount of calculation time. We present a method in which 2-ms time bins of post-stimulus time histograms, rather than every action potential, are the basis for correlation with the acoustic spectrum of the stimulus. In a t test the content of each bin is classified as excitatory, inhibitory or responseless. The bin width is adjusted to the temporal resolution of the units as determined in a gap detection analysis. The method presented here saves a substantial amount of analysis time and reveals adequately spectro-temporal receptive fields.

Neuronal and behavioral sensitivity to binaural time differences in the owl

We demonstrated that ongoing time disparity (OTD) was a sufficient cue for the azimuthal component of receptive fields of auditory neurons in the owl (Tyto alba) midbrain and that OTDs were sufficient to mediate meaningful behavioral responses. We devised a technique which enabled us to change easily between free field and dichotic stimuli while recording from single auditory neurons in the owl mesencephalicus lateralis pars dorsalis (MLD). MLD neurons with restricted spatial receptive fields ("space-mapped neurons") showed marked sensitivity to specific ongoing time disparities. The magnitudes of these disparities were in the behaviorally significant range of tens of microseconds. The ongoing time disparities were correlated significantly with the azimuthal center of receptor fields. Space-mapped neurons were insensitive to transient disparities. MLD neurons which were not space-mapped, i.e., were omnidirectional, did not show any sensitivity to specific OTDs. We confirmed the behavioral relevance of OTD as a cue for localizing a sound in azimuth by presenting OTD differences to tame owls. Using head turning as an assay, we showed that OTD was a sufficient cue for the azimuth of a sound. The relationship between azimuth and OTD obtained from our neurophysiological experiments matched closely the relationship obtained from our behavioral experiments.

Receptive fields of auditory neurons in the owl.

The influence of sound location on the responses of auditory neurons in the forebrain of the owl (Tyto alba) was studied directly by using a remotely controlled, movable sound source under free-field, anechoic conditions. Some auditory neurons demonstrated well-defined receptive fields that were (i) restricted both in elevation and in azimuth and (ii) relatively independent of the intensity and the nature of the sound stimulus. The majority of the fields were located frontally and contralateral to the recording site.