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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.


Related in: MedlinePlus

Two approaches to modeling vesicle release dynamics (figure based on Meddis and Lopez-Poveda 2010). a Model of Westerman and Smith (1988). b Model of Meddis (1986)
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Fig9: Two approaches to modeling vesicle release dynamics (figure based on Meddis and Lopez-Poveda 2010). a Model of Westerman and Smith (1988). b Model of Meddis (1986)

Mentions: Two approaches to modeling the dynamics of neurotransmitter vesicle release have been developed by Westerman and Smith (1988) and Meddis (1986) (Fig. 9). The Westerman approach focuses on implementing a series of three vesicle pools feeding into each other. Each transition is governed by its own time constant, which allows for mimicking observed vesicle dynamics closely. The Meddis approach, in contrast, only has two vesicle pools but therefore also takes endocytosis into account, and is the first vesicle model to do so. Even though both models are structurally different, it was shown that the mathematical description of the resulting vesicle dynamics are closely related (Zhang and Carney 2005). Based on those two fundamental approaches, a series of improvements and refinements thereof have been developed (as reviewed by Meddis and Lopez-Poveda (2010)).Fig. 9


Modeling auditory coding: from sound to spikes.

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

Two approaches to modeling vesicle release dynamics (figure based on Meddis and Lopez-Poveda 2010). a Model of Westerman and Smith (1988). b Model of Meddis (1986)
© Copyright Policy
Related In: Results  -  Collection

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

Fig9: Two approaches to modeling vesicle release dynamics (figure based on Meddis and Lopez-Poveda 2010). a Model of Westerman and Smith (1988). b Model of Meddis (1986)
Mentions: Two approaches to modeling the dynamics of neurotransmitter vesicle release have been developed by Westerman and Smith (1988) and Meddis (1986) (Fig. 9). The Westerman approach focuses on implementing a series of three vesicle pools feeding into each other. Each transition is governed by its own time constant, which allows for mimicking observed vesicle dynamics closely. The Meddis approach, in contrast, only has two vesicle pools but therefore also takes endocytosis into account, and is the first vesicle model to do so. Even though both models are structurally different, it was shown that the mathematical description of the resulting vesicle dynamics are closely related (Zhang and Carney 2005). Based on those two fundamental approaches, a series of improvements and refinements thereof have been developed (as reviewed by Meddis and Lopez-Poveda (2010)).Fig. 9

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.


Related in: MedlinePlus