ReviewCell and tissue research2015
Modeling auditory coding: from sound to spikes.
Review in Cell and tissue research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Computational Model for Synthesizing Auditory Brainstem Responses to Assess Neuronal Alterations in Aging and Autistic Animal Models.Journal of the Association for Research in Otolaryngology : JARO · 2026Article
- Article
- The auditory brainstem response to natural speech is not affected by selective attention.PLoS biology · 2025Article
- Comparing methods for deriving the auditory brainstem response to continuous speech in human listeners.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech.JASA express letters · 2024Article
- A linearized modeling framework for the frequency selectivity in neurons postsynaptic to vibration receptors.Cognitive neurodynamics · 2024Article
- Article
- Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech.bioRxiv : the preprint server for biology · 2024Article
- Auditory Encoding of Natural Speech at Subcortical and Cortical Levels Is Not Indicative of Cognitive Decline.eNeuro · 2024Article
- Subcortical responses to music and speech are alike while cortical responses diverge.Scientific reports · 2024Article
- Article
- Article
- Human discrimination and modeling of high-frequency complex tones shed light on the neural codes for pitch.PLoS computational biology · 2022Article
- A comparative study of eight human auditory models of monaural processing.Acta acustica. European Acoustics Association · 2022Article
- Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem.Frontiers in neuroscience · 2022Article
- Mathematical framework for place coding in the auditory system.PLoS computational biology · 2021Article
- A biomimetic neural encoder for spiking neural network.Nature communications · 2021Article
- A biophysical modelling platform of the cochlear nucleus and other auditory circuits: From channels to networks.Hearing research · 2018Article
- Extraction of Inter-Aural Time Differences Using a Spiking Neuron Network Model of the Medial Superior Olive.Frontiers in neuroscience · 2018Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.