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Modeling auditory coding: from sound to spikes.

Rudnicki M, Schoppe O, Isik M, Völk F, Hemmert W - Cell Tissue Res. (2015)

Bottom Line: On the other hand, discrepancies between model results and measurements reveal gaps in our current knowledge, which can in turn be targeted by matched experiments.Models of the auditory periphery have improved greatly during the last decades, and account for many phenomena observed in experiments.It also provides uniform evaluation and visualization scripts, which allow for direct comparisons between models.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Technische Universität München, München, Germany.

ABSTRACT
Models are valuable tools to assess how deeply we understand complex systems: only if we are able to replicate the output of a system based on the function of its subcomponents can we assume that we have probably grasped its principles of operation. On the other hand, discrepancies between model results and measurements reveal gaps in our current knowledge, which can in turn be targeted by matched experiments. Models of the auditory periphery have improved greatly during the last decades, and account for many phenomena observed in experiments. While the cochlea is only partly accessible in experiments, models can extrapolate its behavior without gap from base to apex and with arbitrary input signals. With models we can for example evaluate speech coding with large speech databases, which is not possible experimentally, and models have been tuned to replicate features of the human hearing organ, for which practically no invasive electrophysiological measurements are available. Auditory models have become instrumental in evaluating models of neuronal sound processing in the auditory brainstem and even at higher levels, where they are used to provide realistic input, and finally, models can be used to illustrate how such a complicated system as the inner ear works by visualizing its responses. The big advantage there is that intermediate steps in various domains (mechanical, electrical, and chemical) are available, such that a consistent picture of the evolvement of its output can be drawn. However, it must be kept in mind that no model is able to replicate all physiological characteristics (yet) and therefore it is critical to choose the most appropriate model-or models-for every research question. To facilitate this task, this paper not only reviews three recent auditory models, it also introduces a framework that allows researchers to easily switch between models. It also provides uniform evaluation and visualization scripts, which allow for direct comparisons between models.

No MeSH data available.


Phase-locking of HRS fibers, measured with the synchronization index, of high spontaneous-rate fibers along the length of the cochlea to pure tones at CF. Sound levels were adjusted 20 dB above the fibers thresholds. Dots indicate physiological data from Johnson (1980)
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Fig15: Phase-locking of HRS fibers, measured with the synchronization index, of high spontaneous-rate fibers along the length of the cochlea to pure tones at CF. Sound levels were adjusted 20 dB above the fibers thresholds. Dots indicate physiological data from Johnson (1980)

Mentions: Phase-locking was analyzed in Fig. 15. The phenomenological model from Zilany et al. (2014) was fit to replicate physiological data, whereas the other two models rely more on the replication of the most important physiological processes involved. Zilany’s model achieved good phase-locking up to high frequencies and the rapid decline of the synchronization index, which was observed in experiments (e.g., data from a cat in Johnson (1980)). This was realized by introducing a fourth-order low-pass. The physiological oriented models that have only implemented the first-order IHC membrane low pass and integrate Ca 2+ influx to Ca 2+ concentration with a single integration time constant exhibit a more gradual decline.Fig. 15


Modeling auditory coding: from sound to spikes.

Rudnicki M, Schoppe O, Isik M, Völk F, Hemmert W - Cell Tissue Res. (2015)

Phase-locking of HRS fibers, measured with the synchronization index, of high spontaneous-rate fibers along the length of the cochlea to pure tones at CF. Sound levels were adjusted 20 dB above the fibers thresholds. Dots indicate physiological data from Johnson (1980)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4487355&req=5

Fig15: Phase-locking of HRS fibers, measured with the synchronization index, of high spontaneous-rate fibers along the length of the cochlea to pure tones at CF. Sound levels were adjusted 20 dB above the fibers thresholds. Dots indicate physiological data from Johnson (1980)
Mentions: Phase-locking was analyzed in Fig. 15. The phenomenological model from Zilany et al. (2014) was fit to replicate physiological data, whereas the other two models rely more on the replication of the most important physiological processes involved. Zilany’s model achieved good phase-locking up to high frequencies and the rapid decline of the synchronization index, which was observed in experiments (e.g., data from a cat in Johnson (1980)). This was realized by introducing a fourth-order low-pass. The physiological oriented models that have only implemented the first-order IHC membrane low pass and integrate Ca 2+ influx to Ca 2+ concentration with a single integration time constant exhibit a more gradual decline.Fig. 15

Bottom Line: On the other hand, discrepancies between model results and measurements reveal gaps in our current knowledge, which can in turn be targeted by matched experiments.Models of the auditory periphery have improved greatly during the last decades, and account for many phenomena observed in experiments.It also provides uniform evaluation and visualization scripts, which allow for direct comparisons between models.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Technische Universität München, München, Germany.

ABSTRACT
Models are valuable tools to assess how deeply we understand complex systems: only if we are able to replicate the output of a system based on the function of its subcomponents can we assume that we have probably grasped its principles of operation. On the other hand, discrepancies between model results and measurements reveal gaps in our current knowledge, which can in turn be targeted by matched experiments. Models of the auditory periphery have improved greatly during the last decades, and account for many phenomena observed in experiments. While the cochlea is only partly accessible in experiments, models can extrapolate its behavior without gap from base to apex and with arbitrary input signals. With models we can for example evaluate speech coding with large speech databases, which is not possible experimentally, and models have been tuned to replicate features of the human hearing organ, for which practically no invasive electrophysiological measurements are available. Auditory models have become instrumental in evaluating models of neuronal sound processing in the auditory brainstem and even at higher levels, where they are used to provide realistic input, and finally, models can be used to illustrate how such a complicated system as the inner ear works by visualizing its responses. The big advantage there is that intermediate steps in various domains (mechanical, electrical, and chemical) are available, such that a consistent picture of the evolvement of its output can be drawn. However, it must be kept in mind that no model is able to replicate all physiological characteristics (yet) and therefore it is critical to choose the most appropriate model-or models-for every research question. To facilitate this task, this paper not only reviews three recent auditory models, it also introduces a framework that allows researchers to easily switch between models. It also provides uniform evaluation and visualization scripts, which allow for direct comparisons between models.

No MeSH data available.