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Multi-timescale neural dynamics for multisensory integration
DOI:10.1038/s41583-024-00845-7.png)
摘要
En 中文
Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms that mediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered. How the brain routinely processes information from different sensory modalities during everyday tasks is not well understood. In this Perspective, Engel and Senkowski propose how oscillatory neural mechanisms operating at multiple timescales within and across brain networks can mediate such multisensory integration.
Keyword:
RANGE TEMPORAL CORRELATIONS
NEURONAL OSCILLATIONS
AUDITORY-CORTEX
PHASE RESET
COMPUTATIONAL PRINCIPLES
INTERSENSORY ATTENTION
UNISENSORY INTEGRATION
SELECTIVE ATTENTION
ALPHA OSCILLATIONS
CORTICAL RESPONSES
期刊
IF:
26.7
论文数:
4.0K
被引数:
4.8W
机构
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