时间：11月12日（周四） 18:00-20:00

地点：恕园28号楼205室

主题：文献汇报

1、文献汇报：音乐专长对音乐解耦与情境的神经编码的影响：基于生成式音乐模型的研究（The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models）

主讲：蒋佳怡 同学

2、文献汇报：人类纹状体非侵入性刺激干扰运动技巧的强化学习（Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills）

主讲：聂淑琴 同学

3、文献汇报：压缩学习通过搭建高阶网络架构来促进人类知识习得（Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition）

主讲：李思琪 同学

欢迎老师与同学们踊跃出席！

脑科学研究所

文献详细信息：

1、题目：The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models

期刊名称：Nature Communications  IF: 18.1  SCI: Q1  中科院：1区

摘要：Music perception requires integrating individual notes into their broader musical context, yet how musical expertise shapes the neural encoding of this information across the auditory hierarchy remains unclear. Here, we address this by using the hierarchical representations from a generative music transformer model to predict human brain activity. We recorded scalp electroencephalography (EEG) from expert musicians and non-musicians, as well as intracranial EEG (iEEG) from six neurological patients, during listening to classical piano pieces. We found that deeper layers of the transformer, which represent more disentangled and contextual musical features, were more predictive of neural responses in both groups. However, this neural correspondence was significantly enhanced by musical expertise: for non-musicians, prediction accuracy plateaued across the model’s final layers, whereas for musicians it continued to increase. This enhanced encoding in experts was also strongly lateralized to the left hemisphere. Finally, the iEEG recordings revealed an anatomical gradient for this function, where neural sites progressively farther from the primary auditory cortex encoded musical context more strongly. Our study reveals how musical training refines the hierarchical neural processing of music and provides a neuro-computational account of this remarkable cognitive skill.

2、题目：Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

期刊名称：Nature Human Behaviour  IF: 17.5  SCI: Q1  中科院：1区

摘要：Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, we exploited a recently developed non-invasive deep brain stimulation technique called transcranial temporal interference stimulation (tTIS) during reinforcement motor learning with concurrent neuroimaging, in a randomized, sham-controlled, double-blind study. Striatal tTIS applied at 80 Hz, but not at 20 Hz, abolished the benefits of reinforcement on motor learning. This effect was related to a selective modulation of neural activity within the striatum. Moreover, 80 Hz, but not 20 Hz, tTIS increased the neuromodulatory influence of the striatum on frontal areas involved in reinforcement motor learning. These results show that tTIS can non-invasively and selectively modulate a striatal mechanism involved in reinforcement learning, expanding our tools for the study of causal relationships between deep brain structures and human behaviour.

3、题目：Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition

期刊名称：nature communications  IF: 18.1  SCI: Q1  中科院：1区

摘要：Humans naturally seek knowledge, yet integrating vast, fragmented information remains challenging. Traditionally, knowledge acquisition has relied on random walks within network—an unguided and inefficient process. Here, we introduce “compressive learning”, a conceptual framework that embeds higher-order structural features—specifically node-degree inhomogeneity—into pre-learning trajectories to scaffold more efficient learning. We show that scale-free networks, owing to their pronounced degree inhomogeneity, are more compressible and more learnable than other network types. Critically, pre-learning paths that highlight this inhomogeneous higher-order structure facilitate subsequent network learning. Magnetoencephalography (MEG) recordings reveal that compressive pre-learning enhances structured neural representations in the dorsal anterior cingulate cortex (ACC). Two-stage computational modeling indicates that compressive learning constructs a network skeleton defined by higher-order structure that efficiently accommodates new inputs. Together, our results highlight the central role of higher-order network structure in human learning and offer a strategic approach to effectively integrating fragmented information into a coherent knowledge framework.