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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

Adaptation of auditory nerve activity for a constant stimulus. a Simplified morphology of the neural response to a constant stimulus (gray bar); the peak response occurs with a small delay after stimulus onset and then decays rapidly with a short time constant (1), which is followed by a longer, sustained decay with a longer time constant (2); after stimulus offset, activity plummets clearly below the spontaneous rate and - after a dead time - slowly recovers (3); (adapted from Zhang and Carney 2005). b Peri-Stimulus Time Histogram (PSTH) for a constant stimulus of 50 ms duration; gray lines show the recordings of 46 auditory nerve fibers from the ferret at their characteristic frequency at 35-45 dB (SPL); the black line shows the mean value (based on Sumner and Palmer 2012)
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Fig8: Adaptation of auditory nerve activity for a constant stimulus. a Simplified morphology of the neural response to a constant stimulus (gray bar); the peak response occurs with a small delay after stimulus onset and then decays rapidly with a short time constant (1), which is followed by a longer, sustained decay with a longer time constant (2); after stimulus offset, activity plummets clearly below the spontaneous rate and - after a dead time - slowly recovers (3); (adapted from Zhang and Carney 2005). b Peri-Stimulus Time Histogram (PSTH) for a constant stimulus of 50 ms duration; gray lines show the recordings of 46 auditory nerve fibers from the ferret at their characteristic frequency at 35-45 dB (SPL); the black line shows the mean value (based on Sumner and Palmer 2012)

Mentions: A fundamental phenomenon observed in all sensory systems is (onset) adaptation. This term describes the widely observed neural reaction to constant stimuli, which is characterized by a very high firing rate at the onset of the stimulus that quickly decays to a much lower (or adapted) rate of firing. The auditory nerve is not an exception and also shows this type of neural response, having far-reaching implications on neural information processing in higher stages of the auditory system (Perez-Gonzalez and Malmierca 2014). For example, the degree of onset adaptation of the firing rate depends on the characteristic frequency of the fiber and thereby contributes to mechanisms for maintaining efficient coding of temporal information such as phase-locking (Sumner and Palmer 2012; Perez-Gonzalez and Malmierca 2014). The typical time course of onset adaptation can be seen in Fig. 8, left panel (arrow heads 1 and 2).Fig. 8


Modeling auditory coding: from sound to spikes.

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

Adaptation of auditory nerve activity for a constant stimulus. a Simplified morphology of the neural response to a constant stimulus (gray bar); the peak response occurs with a small delay after stimulus onset and then decays rapidly with a short time constant (1), which is followed by a longer, sustained decay with a longer time constant (2); after stimulus offset, activity plummets clearly below the spontaneous rate and - after a dead time - slowly recovers (3); (adapted from Zhang and Carney 2005). b Peri-Stimulus Time Histogram (PSTH) for a constant stimulus of 50 ms duration; gray lines show the recordings of 46 auditory nerve fibers from the ferret at their characteristic frequency at 35-45 dB (SPL); the black line shows the mean value (based on Sumner and Palmer 2012)
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4487355&req=5

Fig8: Adaptation of auditory nerve activity for a constant stimulus. a Simplified morphology of the neural response to a constant stimulus (gray bar); the peak response occurs with a small delay after stimulus onset and then decays rapidly with a short time constant (1), which is followed by a longer, sustained decay with a longer time constant (2); after stimulus offset, activity plummets clearly below the spontaneous rate and - after a dead time - slowly recovers (3); (adapted from Zhang and Carney 2005). b Peri-Stimulus Time Histogram (PSTH) for a constant stimulus of 50 ms duration; gray lines show the recordings of 46 auditory nerve fibers from the ferret at their characteristic frequency at 35-45 dB (SPL); the black line shows the mean value (based on Sumner and Palmer 2012)
Mentions: A fundamental phenomenon observed in all sensory systems is (onset) adaptation. This term describes the widely observed neural reaction to constant stimuli, which is characterized by a very high firing rate at the onset of the stimulus that quickly decays to a much lower (or adapted) rate of firing. The auditory nerve is not an exception and also shows this type of neural response, having far-reaching implications on neural information processing in higher stages of the auditory system (Perez-Gonzalez and Malmierca 2014). For example, the degree of onset adaptation of the firing rate depends on the characteristic frequency of the fiber and thereby contributes to mechanisms for maintaining efficient coding of temporal information such as phase-locking (Sumner and Palmer 2012; Perez-Gonzalez and Malmierca 2014). The typical time course of onset adaptation can be seen in Fig. 8, left panel (arrow heads 1 and 2).Fig. 8

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