Dataset Information

Pre-target neural oscillations predict variability in the detection of small pitch changes.

ABSTRACT: Pitch discrimination is important for language or music processing. Previous studies indicate that auditory perception depends on pre-target neural activity. However, so far the pre-target electrophysiological conditions which enable the detection of small pitch changes are not well studied, but might yield important insights into pitch-processing. We used magnetoencephalography (MEG) source imaging to reveal the pre-target effects of successful auditory detection of small pitch deviations from a sequence of standard tones. Participants heard a sequence of four pure tones and had to determine whether the last target tone was different or identical to the first three standard sounds. We found that successful pitch change detection could be predicted from the amplitude of theta (4-8 Hz) oscillatory activity in the right inferior frontal gyrus (IFG) as well as beta (12-30 Hz) oscillatory activity in the right auditory cortex. These findings confirm and extend evidence for the involvement of theta as well as beta-band activity in auditory perception.

SUBMITTER: Florin E

PROVIDER: S-EPMC5436812 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Pre-target neural oscillations predict variability in the detection of small pitch changes.

Florin Esther E Vuvan Dominique D Peretz Isabelle I Baillet Sylvain S

PloS one 20170518 5

Pitch discrimination is important for language or music processing. Previous studies indicate that auditory perception depends on pre-target neural activity. However, so far the pre-target electrophysiological conditions which enable the detection of small pitch changes are not well studied, but might yield important insights into pitch-processing. We used magnetoencephalography (MEG) source imaging to reveal the pre-target effects of successful auditory detection of small pitch deviations from ...[more]

PMID: 28542644

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Project description:Building an efficient and reliable small target motion detection visual system is challenging for artificial intelligence robotics because a small target only occupies few pixels and hardly displays visual features in images. Biological visual systems that have evolved over millions of years could be ideal templates for designing artificial visual systems. Insects benefit from a class of specialized neurons, called small target motion detectors (STMDs), which endow them with an excellent ability to detect small moving targets against a cluttered dynamic environment. Some bio-inspired models featured in feed-forward information processing architectures have been proposed to imitate the functions of the STMD neurons. However, feedback, a crucial mechanism for visual system regulation, has not been investigated deeply in the STMD-based neural circuits and its roles in small target motion detection remain unclear. In this paper, we propose a time-delay feedback STMD model for small target motion detection in complex backgrounds. The main contributions of this study are as follows. First, a feedback pathway is designed by transmitting information from output-layer neurons to lower-layer interneurons in the STMD pathway and the role of the feedback is analyzed from the view of mathematical analysis. Second, to estimate the feedback constant, the existence and uniqueness of solutions for nonlinear dynamical systems formed by feedback loop are analyzed via Schauder's fixed point theorem and contraction mapping theorem. Finally, an iterative algorithm is designed to solve the nonlinear problem and the performance of the proposed model is tested by experiments. Experimental results demonstrate that the feedback is able to weaken background false positives while maintaining a minor effect on small targets. It outperforms existing STMD-based models regarding the accuracy of fast-moving small target detection in visual clutter. The proposed feedback approach could inspire the relevant modeling of robust motion perception robotics visual systems.

Dataset Information

Pre-target neural oscillations predict variability in the detection of small pitch changes.

Publications

Pre-target neural oscillations predict variability in the detection of small pitch changes.

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