Denoising techniques reveal neural correlates of modulation masking release in auditory cortex

Hearing aids allow hearing impaired (HI) individuals to regain auditory perception in quiet settings. However, despite advances in hearing aid technology, HI individuals do not perform as well in situations with background sound as normally hearing (NH) listeners. An extensive literature demonstrates that when comparing tone detection performance in background noise, NH listeners have better thresholds when that noise is temporally modulated as compared to temporally unmodulated. However, this perceptual benefit, called Modulation Masking Release (MMR), is much reduced in HI listeners, and this is thought to be a reason for why HI listeners struggle in the presence of background sound. This study explores neural correlates of MMR in NH and HI gerbils. Trained, awake gerbils (Meriones unguiculatus) listen passively to a target tone (1 kHz) embedded in modulated or unmodulated noise while a 16-channel microelectrode array records multi-unit neural spike activity in core auditory cortex. In addition, microelectrodes also record nuisance signals due to animal movements and interference in the wireless recording setup. The current study examines the potency of three different denoising algorithms using signal detection theory. The first, amplitude rejection (AR) classifies events based on amplitude. The second, virtual referencing (VR) applies subtraction of a virtual common ground signal. The third, inter-electrode correlation (IEC) compares events across electrodes to decide whether to classify an event as noise or as spike. Using Receiver-Operator-Characteristics (ROC), these classifiers were ranked. Results suggest that combining IEC and VR leads to best denoising performance. Denoised spike train reveals a robust correlation of spike rate with behavioral performance. Results hint that neural correlates of MMR are not primarily based on spike rate coding, at least in the core auditory cortex.

Name thesis:degree_name

Master of Science in Biomedical Engineering - (M.S.)

Discipline thesis:degree_discipline

Biomedical Engineering

Year

2017

Author dc:creator

• Chaubal, Sahil

Contributors dc:contributor

• Antje Ihlefeld
• Richard A. Foulds
• Mesut Sahin

• Modulation masking release
• Denoising algorithms
• Biomedical Engineering and Bioengineering

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/1

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1000