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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

Kinetics of vesicle fusion shows very strong [Ca +2] i dependence, figure from Hemmert et al. 2003.
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Fig7: Kinetics of vesicle fusion shows very strong [Ca +2] i dependence, figure from Hemmert et al. 2003.

Mentions: Hair cell ribbon synapses can sustain high rates of vesicle release (Moser et al. 2006a) and are able to synchronize the release of multiple vesicles to produce large AMPA-mediated excitatory postsynaptic currents (Glowatzki and Fuchs 2002). The underlying mechanism for multivesicular release at ribbon synapses is still unknown. Neurotransmitter release from IHCs is triggered by Ca 2+ entry that is carried almost exclusively by Ca V1.3 channels. These channels are voltage sensitive and open upon depolarization of the cell membrane. They are clustered at the presynaptic active zones and colocalized with readily releasable vesicles (Graydon et al. 2011). The Ca V1.3 channels open very rapidly following a stimulus with a delay of about 50 μs, the onset time constant is about 0.18 ms (Zampini et al. 2013). Furthermore, the local calcium concentration is the integral of the calcium influx and therefore also has a low-pass characteristic (Kidd and Weiss 1990). Although the molecular identity of the Ca 2+ sensor is still not identified, it is highly cooperative, requiring the binding of multiple Ca 2+ ions to trigger release (Fig. 6, according to Beutner et al. 2001), resulting in rate constants that are strongly Ca 2+ dependent (see Fig. 7, Hemmert et al. 2003). Taking into account that there will always be a certain calcium concentration above zero, it can be assumed that at least some of the binding sites are expected to be already filled with calcium. Transferring this notion to the kinetic model, it can be expected that the model is already in an advanced state and fewer binding sites have to be filled to reach vesicle fusion, which improves the speed of the vesicle fusion with increasing [Ca +2] i. In order to reach high calcium concentrations, which are required for fast vesicle fusion, it is essential that Ca V1.3 channels are in very close proximity to the vesicle release sites (Graydon et al. 2011).Fig. 6


Modeling auditory coding: from sound to spikes.

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

Kinetics of vesicle fusion shows very strong [Ca +2] i dependence, figure from Hemmert et al. 2003.
© Copyright Policy
Related In: Results  -  Collection

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

Fig7: Kinetics of vesicle fusion shows very strong [Ca +2] i dependence, figure from Hemmert et al. 2003.
Mentions: Hair cell ribbon synapses can sustain high rates of vesicle release (Moser et al. 2006a) and are able to synchronize the release of multiple vesicles to produce large AMPA-mediated excitatory postsynaptic currents (Glowatzki and Fuchs 2002). The underlying mechanism for multivesicular release at ribbon synapses is still unknown. Neurotransmitter release from IHCs is triggered by Ca 2+ entry that is carried almost exclusively by Ca V1.3 channels. These channels are voltage sensitive and open upon depolarization of the cell membrane. They are clustered at the presynaptic active zones and colocalized with readily releasable vesicles (Graydon et al. 2011). The Ca V1.3 channels open very rapidly following a stimulus with a delay of about 50 μs, the onset time constant is about 0.18 ms (Zampini et al. 2013). Furthermore, the local calcium concentration is the integral of the calcium influx and therefore also has a low-pass characteristic (Kidd and Weiss 1990). Although the molecular identity of the Ca 2+ sensor is still not identified, it is highly cooperative, requiring the binding of multiple Ca 2+ ions to trigger release (Fig. 6, according to Beutner et al. 2001), resulting in rate constants that are strongly Ca 2+ dependent (see Fig. 7, Hemmert et al. 2003). Taking into account that there will always be a certain calcium concentration above zero, it can be assumed that at least some of the binding sites are expected to be already filled with calcium. Transferring this notion to the kinetic model, it can be expected that the model is already in an advanced state and fewer binding sites have to be filled to reach vesicle fusion, which improves the speed of the vesicle fusion with increasing [Ca +2] i. In order to reach high calcium concentrations, which are required for fast vesicle fusion, it is essential that Ca V1.3 channels are in very close proximity to the vesicle release sites (Graydon et al. 2011).Fig. 6

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