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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.


Modulation gain at a CF of 8 kHz for stimuli 10 dB above the individual rate threshold (hearing level, HL) for HRS fibers only. The light gray area represents reference data from Joris and Yin 1992
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Fig16: Modulation gain at a CF of 8 kHz for stimuli 10 dB above the individual rate threshold (hearing level, HL) for HRS fibers only. The light gray area represents reference data from Joris and Yin 1992

Mentions: Also the modulation gain of the Zilany et al. (2014) model 10 dB above rate threshold was higher compared to the other models (Fig. 16), which might be due to its offset adaptation. The modulation gain also showed a low-pass characteristic, which is – in contrast to phase-locking – also dependent on the filter bandwidth. This is why the point of high-frequency roll-off was lowest for the Holmberg et al. (2007) model, followed by the Zilany et al. (2014) and then the Meddis (2014) model. From physiological recordings (Joris and Yin 1992) it is known that the modulation gain decreases at higher levels.Fig. 16


Modeling auditory coding: from sound to spikes.

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

Modulation gain at a CF of 8 kHz for stimuli 10 dB above the individual rate threshold (hearing level, HL) for HRS fibers only. The light gray area represents reference data from Joris and Yin 1992
© Copyright Policy
Related In: Results  -  Collection

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

Fig16: Modulation gain at a CF of 8 kHz for stimuli 10 dB above the individual rate threshold (hearing level, HL) for HRS fibers only. The light gray area represents reference data from Joris and Yin 1992
Mentions: Also the modulation gain of the Zilany et al. (2014) model 10 dB above rate threshold was higher compared to the other models (Fig. 16), which might be due to its offset adaptation. The modulation gain also showed a low-pass characteristic, which is – in contrast to phase-locking – also dependent on the filter bandwidth. This is why the point of high-frequency roll-off was lowest for the Holmberg et al. (2007) model, followed by the Zilany et al. (2014) and then the Meddis (2014) model. From physiological recordings (Joris and Yin 1992) it is known that the modulation gain decreases at higher levels.Fig. 16

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.