Evidence map›Paper›PMID 26347643›Full record

ArticleFrontiers in computational neuroscience2015

Modeling attention-driven plasticity in auditory cortical receptive fields.

Michael A Carlin, Mounya Elhilali

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Attention-Driven Modulation of Auditory Cortex Activity during Selective Listening in a Multispeaker Setting.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  2. Article
  3. Article
  4. Modelling auditory attention.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017
    Review
  5. A Framework for Speech Activity Detection Using Adaptive Auditory Receptive Fields.IEEE/ACM transactions on audio, speech, and language processing · 2015
    Article
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

2 authors.

Michael A CarlinLaboratory for Computational Audio Perception, Department of Electrical and Computer Engineering, Johns Hopkins University Baltimore, MD, USA.
Mounya ElhilaliLaboratory for Computational Audio Perception, Department of Electrical and Computer Engineering, Johns Hopkins University Baltimore, MD, USA.

Funding

Overcoming the Cocktail Party Problem: A Multi-scale Perspective on the NeurobioR01AG036424 · NIA · JOHNS HOPKINS UNIVERSITY · PI ELHILALI, MOUNYA · 2010 to 2014
$2.2M
NIA NIH HHS R01 AG036424
6 · The paper itself

Abstract

To navigate complex acoustic environments, listeners adapt neural processes to focus on behaviorally relevant sounds in the acoustic foreground while minimizing the impact of distractors in the background, an ability referred to as top-down selective attention. Particularly striking examples of attention-driven plasticity have been reported in primary auditory cortex via dynamic reshaping of spectro-temporal receptive fields (STRFs). By enhancing the neural response to features of the foreground while suppressing those to the background, STRFs can act as adaptive contrast matched filters that directly contribute to an improved cognitive segregation between behaviorally relevant and irrelevant sounds. In this study, we propose a novel discriminative framework for modeling attention-driven plasticity of STRFs in primary auditory cortex. The model describes a general strategy for cortical plasticity via an optimization that maximizes discriminability between the foreground and distractors while maintaining a degree of stability in the cortical representation. The first instantiation of the model describes a form of feature-based attention and yields STRF adaptation patterns consistent with a contrast matched filter previously reported in neurophysiological studies. An extension of the model captures a form of object-based attention, where top-down signals act on an abstracted representation of the sensory input characterized in the modulation domain. The object-based model makes explicit predictions in line with limited neurophysiological data currently available but can be readily evaluated experimentally. Finally, we draw parallels between the model and anatomical circuits reported to be engaged during active attention. The proposed model strongly suggests an interpretation of attention-driven plasticity as a discriminative adaptation operating at the level of sensory cortex, in line with similar strategies previously described across different sensory modalities.

Indexed as

auditory attentioncomputational modelfeature-based attentionobject-based attentionplasticityspectro-temporal receptive fields

Identifiers

PMID26347643
PMCPMC4541291

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.