In auditory modeling, we use digital signal processing algorithms to simulate the functionality of the nuclei in the human auditory system. Hence, we first design the models so that they are able to reproduce response characteristics of various nuclei based on neurophysiological data. Thereafter, we employ these models to predict human perception in different conditions.
Our special interest lies in modeling the response of the auditory nerve fiber (ANF) to electrical stimulation in cochlear implants ([Seeber & Bruce; 2016] and [Takanen, Bruce & Seeber; 2016]) and the percepts of implant users. To this end, we have recently developed a novel phenomenological model for the electrically stimulated ANF as an extension of our previous single pulse model ([Horne, Seeber; 2016]), that is capable of reproducing response characteristics for pulse train stimuli where individual pulses interact on a neural level. This model takes into account the effects of refractoriness, facilitation and accommodation in the behavior of the ANF ([Takanen & Seeber, CIAP 2017], [Takanen & Seeber, ASA 2017], [Takanen & Seeber, Bernstein Conf. 2017], [Takanen & Seeber, DAGA 2017]).
The ongoing cochlear implant research at the AIP also aims to put into practice the work developed in the auditory modeling area, towards individually fitted models for implantees and using models to improve the stimulation strategies of cochlear implants ([Werner & Seeber, CIAP 2017], [Werner & Seeber, Bernstein Conf. 2017]). The development of the fitting procedure and validation of a patient-specific model is based on psychophysical and electrophysiological measurements carried out at the AIP. The objective is to obtain a model that represents spatial and temporal responses of individual auditory nerves, and use this approach to develop and evaluate stimulation algorithms with the aim to improve speech understanding in critical conditions.
Past: Kauê Werner
2011-2014: Colin Horne, Medical Research Council UK studentship
2012-2017: Bernstein Center for Computational Neuroscience Munich, project C5 (BMBF grant 01 GQ 1004B).
2016-2017: Smartstart Grant in Computational Neurosciences for Kauê Werner
2017-2018: DAAD Grant to Kauê Werner
- Seeber, B.U.; Bruce, I.: The history and future of neural modelling for cochlear implants. Network: Computation in Neural Systems (Vol. 27, Issue 2-3), 2016, 53-66
- Takanen, M.; Bruce, I.C.; Seeber, B.U.: Phenomenological modelling of electrically stimulated auditory nerve fibers: A review. Network: Computation in Neural Systems (Vol. 27, Issue 2-3), 2016, 157-185
- Horne, C.; Sumner, C.S.; Seeber, B.U.: A phenomenological model of the electrically stimulated auditory nerve fiber: temporal and biphasic response properties. Frontiers in Computational Neuroscience (Vol. 10, No. 8), 2016
- Takanen, M.; Seeber, B.U.: A Phenomenological Model for Predicting Responses of Electrically Stimulated Auditory Nerve Fiber to Ongoing Pulsatile Stimulation. Conf. on Implantable Auditory Prostheses, 2017, 91, M11a
- Takanen, M.; Seeber, B.U.: Reproducing response characteristics of electrically-stimulated auditory nerve fibers with a phenomenological model. JASA, Acoustics '17 Boston, 2017, 3629-3630, 2pPPa4
- Takanen, M.; Seeber, B.U.: Phenomenological modeling of the electrically stimulated binaural auditory system. Proc. Bernstein Conference, 2017, T94
- Takanen, M.; Weller, J.-N.; Seeber, B.U.: Modeling Refractoriness In Phenomenological Models of Electrically-Stimulated Auditory Nerve Fibers. Fortschritte der Akustik -- DAGA '17, 2017, 468-470
- Werner, K.; Leibold, C.; Seeber, B.U.: Individual fitting and prediction with a phenomenological auditory nerve fiber model for CI users. Conf. on Implantable Auditory Prostheses, CIAP, 2017, p. 92, M11b
- Werner, K.; Leibold, C.; Seeber, B.U.: Individual fitting of neural population latency distribution with a phenomenological auditory nerve fiber model for cochlear implant users. Proc. Bernstein Conference, 2017