Real-Time Auditory Nerve Modeling Using System-On-Chip Field-Programmable Gate Arrays

Matthew Blunt, Dr. Ross Snider, Department of Electrical and Computer Engineering, Montana State University, Bozeman MT USA 59717

For well over forty years, numerous researchers have provided meaningful contributions to auditory nerve research. This research has culminated in a phenomenological model of the synapse between the inner hair cell and auditory nerve. However, the latest version of this model replicates the behavior of only a single auditory nerve in MATLAB. Therefore, to simulate multiple auditory nerves, researchers are limited to running multiple models sequentially. As the number of auditory nerves increases, these simulations become infeasible due to long runtimes. To address this problem, we are developing a hardware-accelerated version of the auditory nerve model that runs on an open source System-on-Chip (SoC) Field Programmable Gate Array (FPGA) development platform. Due to the inherently parallel nature and low, deterministic latency of FPGAs, our approach allows for multiple auditory nerve models to run simultaneously in real-time. We demonstrate multiple auditory nerve responses to live audio signals running on our hardware platform in real-time. Modeling multiple auditory nerve fibers in real-time will provide important insight into model accuracy under more realistic conditions. In addition, our hardware-accelerated model will allow researchers to edit model parameters and view differences in nerve responses in real-time, enabling faster development of better auditory nerve models. In the future, the open-source SoC FPGA platform will provide auditory researchers with a low-cost, high-performance tool with which to develop and test new models and algorithms.

Additional Abstract Information

Presenter: Matthew Blunt

Institution: Montana State University Bozeman

Type: Poster

Subject: Electrical & Computer Engineering

Status: Approved

Time and Location

Session: Poster 6
Date/Time: Tue 2:00pm-3:00pm
Session Number: 4530