Learning hierarchical sequence representations across human cortex and hippocampus.

Learning hierarchical sequence representations across human cortex and hippocampus.
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DOI:
10.1126/sciadv.abc4530
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发表时间:
2021-03
期刊:
影响因子:
13.6
通讯作者:
Melloni L
Melloni L
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Henin S;Turk-Browne NB;Friedman D;Liu A;Dugan P;Flinker A;Doyle W;Devinsky O;Melloni L

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序列学习在整个人类大脑中被跟踪,用于解析序列的计算系统被突出显示。感觉输入以人类作为分段单元体验的连续序列到达,例如,话语和事件。大脑发现记忆的能力被称为统计学习。结构可以在多个级别上表示,包括转移概率、顺序位置和单元标识。为了研究皮层和海马的序列编码,我们记录了人类受试者的颅内电极,因为他们暴露于包含颞叶的听觉和视觉序列。我们在几分钟内发现了神经跟踪,在大脑区域有特征性的轮廓。早期处理跟踪较低级别的特征(例如,音节)和学习单元(例如,单词),而随后的处理仅跟踪学习的单元。学习快速成形的神经表征,从编码过渡概率的早期大脑区域到编码顺序位置和单位身份的关联区域和海马体,具有复杂性梯度。这些研究结果表明,存在多个,并行的计算系统的顺序学习分层组织的皮质海马电路。
Sequence learning is tracked across the human brain, and the computational systems used to parse the sequences are highlighted. Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain’s ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.
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