Evidence map›Paper›PMID 25761097›Full record

ArticlePLoS biology2015

A neural mechanism for time-window separation resolves ambiguity of adaptive coding.

K Jannis Hildebrandt, Bernhard Ronacher, R Matthias Hennig, Jan Benda

Abstract read
In one paragraph

Article in PLoS biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

K Jannis HildebrandtCluster of Excellence "Hearing4all", Department for Neuroscience, University of Oldenburg, Oldenburg, Germany.
Bernhard RonacherDepartment of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
R Matthias HennigDepartment of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
Jan BendaInstitute for Neurobiology, Eberhard Karls Universität Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The senses of animals are confronted with changing environments and different contexts. Neural adaptation is one important tool to adjust sensitivity to varying intensity ranges. For instance, in a quiet night outdoors, our hearing is more sensitive than when we are confronted with the plurality of sounds in a large city during the day. However, adaptation also removes available information on absolute sound levels and may thus cause ambiguity. Experimental data on the trade-off between benefits and loss through adaptation is scarce and very few mechanisms have been proposed to resolve it. We present an example where adaptation is beneficial for one task--namely, the reliable encoding of the pattern of an acoustic signal-but detrimental for another--the localization of the same acoustic stimulus. With a combination of neurophysiological data, modeling, and behavioral tests, we show that adaptation in the periphery of the auditory pathway of grasshoppers enables intensity-invariant coding of amplitude modulations, but at the same time, degrades information available for sound localization. We demonstrate how focusing the response of localization neurons to the onset of relevant signals separates processing of localization and pattern information temporally. In this way, the ambiguity of adaptive coding can be circumvented and both absolute and relative levels can be processed using the same set of peripheral neurons.

Indexed as

Acoustic StimulationAction PotentialsAdaptation, PhysiologicalAnimalsAuditory PathwaysAuditory PerceptionCochlear NerveFemaleGrasshoppersMaleNeuronsSoundSound LocalizationTime Factors

Identifiers

PMID25761097
PMCPMC4356587

What OpenQuestion holds

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Registered trials

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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.