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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.


Comparison of rate-tuning curves for two fibers with CFs of 500 Hz (solid lines) and 2 kHz (dashed lines). Peaks and drops in the filter responses are due to nonlinearities in the models
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Fig12: Comparison of rate-tuning curves for two fibers with CFs of 500 Hz (solid lines) and 2 kHz (dashed lines). Peaks and drops in the filter responses are due to nonlinearities in the models

Mentions: Larger differences between the models are apparent in the tuning curves plotted in Fig. 12. Human/primate tuning curves might be sharper than in other mammals (Shera et al. 2002) (although this is debated, see Ruggero and Temchin 2005; Lopez-Poveda and Eustaquio-Martin 2013). The models of Holmberg et al. ((2007) and Zilany et al. (2014) have implemented sharper tuning curves compared to the Meddis (2014) model, as they were tuned to the psychoacoustic measurements from Shera et al. (2002). As the Holmberg et al. (2007) model is based on a travelingwave model with four second-order low-pass filters on top, it reaches very steep high-frequency slopes (up to about 200 dB/oct), where the slopes of the other filter-based models are limited due to their lower filter order.Fig. 12


Modeling auditory coding: from sound to spikes.

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

Comparison of rate-tuning curves for two fibers with CFs of 500 Hz (solid lines) and 2 kHz (dashed lines). Peaks and drops in the filter responses are due to nonlinearities in the models
© Copyright Policy
Related In: Results  -  Collection

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

Fig12: Comparison of rate-tuning curves for two fibers with CFs of 500 Hz (solid lines) and 2 kHz (dashed lines). Peaks and drops in the filter responses are due to nonlinearities in the models
Mentions: Larger differences between the models are apparent in the tuning curves plotted in Fig. 12. Human/primate tuning curves might be sharper than in other mammals (Shera et al. 2002) (although this is debated, see Ruggero and Temchin 2005; Lopez-Poveda and Eustaquio-Martin 2013). The models of Holmberg et al. ((2007) and Zilany et al. (2014) have implemented sharper tuning curves compared to the Meddis (2014) model, as they were tuned to the psychoacoustic measurements from Shera et al. (2002). As the Holmberg et al. (2007) model is based on a travelingwave model with four second-order low-pass filters on top, it reaches very steep high-frequency slopes (up to about 200 dB/oct), where the slopes of the other filter-based models are limited due to their lower filter order.Fig. 12

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.