ArticleFrontiers in computational neuroscience2015
Modeling attention-driven plasticity in auditory cortical receptive fields.
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.
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Who cites it
5 citing papers in PubMed.
- 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 · 2024Article
- Familiarization with meaningless sound patterns facilitates learning to detect those patterns among distracters.Frontiers in psychology · 2022Article
- AIM: A network model of attention in auditory cortex.PLoS computational biology · 2021Article
- Modelling auditory attention.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- A Framework for Speech Activity Detection Using Adaptive Auditory Receptive Fields.IEEE/ACM transactions on audio, speech, and language processing · 2015Article
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Authors and funding
2 authors.
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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.
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