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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.


Left panel: auditory nerve firing rate as a function of sound pressure level for high, medium and low spontaneous rate fibers (based on Sumner et al. 2002). Scatter plots show physiological data from Winter et al. 1990, solid lines the prediction by the model. Right panel: conceptual difference between dynamic range adaptation and firing rate adaptation as suggested by Wen et al. (2012)
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Fig10: Left panel: auditory nerve firing rate as a function of sound pressure level for high, medium and low spontaneous rate fibers (based on Sumner et al. 2002). Scatter plots show physiological data from Winter et al. 1990, solid lines the prediction by the model. Right panel: conceptual difference between dynamic range adaptation and firing rate adaptation as suggested by Wen et al. (2012)

Mentions: As previously mentioned, the AC/DC ratio of the transmembrane voltage grows expansively for small and compressively for medium to high sound pressure levels (Patuzzi and Sellick 1983). More specifically, both components show saturation for high stereocilia displacements above 100 nm, but different growth slopes for smaller displacements. Model simulations suggest that the compressive transfer function between stereocilia displacement and transmembrane voltage can be attributed to potassium channels and this compression already occurs before saturation of mechanoelectrical transduction is reached (Lopez-Poveda and Eustaquio-Martin 2006). Coming from a top-down perspective, a very different approach is to analyze auditory nerve activity in terms of firing rate as a function of sound pressure level. While recent studies suggest that auditory nerve fiber activity generally follows a third-order function of the stimulus amplitude (Heil 2014), the precise transfer function for auditory nerve activity is more complex and depends on several aspects. Just like the transmembrane voltage, the so-called rate-level functions exhibit saturating behavior, but they very much depend on the type of auditory nerve fiber of interest (Fig. 10, left panel). By fitting free parameters to the spontaneous rate of a given auditory nerve fiber, model predictions of rate-level functions match physiological data closely (Sumner et al. 2002). The obvious differences of dynamic range for different types of fibers are a fundamental basis for efficiently coding signals in the auditory nerve as a whole, given the limits of dynamic range that can be coded by a single nerve fiber. Another basis for increasing efficiency of coding for a wide dynamic range is dynamic range adaptation. One method to assess changes in coding for natural-like situations of changing dynamic ranges is to use stimuli that vary in their distribution of non-uniform sound pressure levels (as described in Dean et al. 2008 for the midbrain). It has been shown that a model with power-law adaptation is also able to explain the time course of adaptation of the mean firing rate and changes in the dynamic range observed in AN responses (Zilany and Carney 2010). A recent study for the auditory nerve revealed that adaptation of the dynamic range occurs simultaneously with firing rate adaptation (Wen et al. 2012). While dynamic range adaptation actually represents a change in coding behavior, firing rate adaptation refers to the well-known concept of decrease of firing rate for ongoing stimuli (Fig. 10, right panel). It could be shown that both types of adaptation roughly occur at the same time scale of 100 ms to 400 ms and that they are interdependent (Wen et al. 2012).Fig. 10


Modeling auditory coding: from sound to spikes.

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

Left panel: auditory nerve firing rate as a function of sound pressure level for high, medium and low spontaneous rate fibers (based on Sumner et al. 2002). Scatter plots show physiological data from Winter et al. 1990, solid lines the prediction by the model. Right panel: conceptual difference between dynamic range adaptation and firing rate adaptation as suggested by Wen et al. (2012)
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Related In: Results  -  Collection

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

Fig10: Left panel: auditory nerve firing rate as a function of sound pressure level for high, medium and low spontaneous rate fibers (based on Sumner et al. 2002). Scatter plots show physiological data from Winter et al. 1990, solid lines the prediction by the model. Right panel: conceptual difference between dynamic range adaptation and firing rate adaptation as suggested by Wen et al. (2012)
Mentions: As previously mentioned, the AC/DC ratio of the transmembrane voltage grows expansively for small and compressively for medium to high sound pressure levels (Patuzzi and Sellick 1983). More specifically, both components show saturation for high stereocilia displacements above 100 nm, but different growth slopes for smaller displacements. Model simulations suggest that the compressive transfer function between stereocilia displacement and transmembrane voltage can be attributed to potassium channels and this compression already occurs before saturation of mechanoelectrical transduction is reached (Lopez-Poveda and Eustaquio-Martin 2006). Coming from a top-down perspective, a very different approach is to analyze auditory nerve activity in terms of firing rate as a function of sound pressure level. While recent studies suggest that auditory nerve fiber activity generally follows a third-order function of the stimulus amplitude (Heil 2014), the precise transfer function for auditory nerve activity is more complex and depends on several aspects. Just like the transmembrane voltage, the so-called rate-level functions exhibit saturating behavior, but they very much depend on the type of auditory nerve fiber of interest (Fig. 10, left panel). By fitting free parameters to the spontaneous rate of a given auditory nerve fiber, model predictions of rate-level functions match physiological data closely (Sumner et al. 2002). The obvious differences of dynamic range for different types of fibers are a fundamental basis for efficiently coding signals in the auditory nerve as a whole, given the limits of dynamic range that can be coded by a single nerve fiber. Another basis for increasing efficiency of coding for a wide dynamic range is dynamic range adaptation. One method to assess changes in coding for natural-like situations of changing dynamic ranges is to use stimuli that vary in their distribution of non-uniform sound pressure levels (as described in Dean et al. 2008 for the midbrain). It has been shown that a model with power-law adaptation is also able to explain the time course of adaptation of the mean firing rate and changes in the dynamic range observed in AN responses (Zilany and Carney 2010). A recent study for the auditory nerve revealed that adaptation of the dynamic range occurs simultaneously with firing rate adaptation (Wen et al. 2012). While dynamic range adaptation actually represents a change in coding behavior, firing rate adaptation refers to the well-known concept of decrease of firing rate for ongoing stimuli (Fig. 10, right panel). It could be shown that both types of adaptation roughly occur at the same time scale of 100 ms to 400 ms and that they are interdependent (Wen et al. 2012).Fig. 10

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.