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NeuroBind: Towards Unified Multimodal Representations for Neural Signals

Autor: Yang, Fengyu, Feng, Chao, Wang, Daniel, Wang, Tianye, Zeng, Ziyao, Xu, Zhiyang, Park, Hyoungseob, Ji, Pengliang, Zhao, Hanbin, Li, Yuanning, Wong, Alex

Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like electrophysiology and neuroimaging, reflects various aspects of informa

Externí odkaz: http://arxiv.org/abs/2407.14020

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Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 years old

Autor: Bian, Lingbin, Wang, Nizhuan, Li, Yuanning, Razi, Adeel, Wang, Qian, Zhang, Han, Shen, Dinggang, Consortium, the UNC/UMN Baby Connectome Project

The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. Traditional methods for estimating brain modularity usually rely on group-averaged functional connect

Externí odkaz: http://arxiv.org/abs/2407.13118

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Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs

Autor: He, Linyang, Chen, Peili, Nie, Ercong, Li, Yuanning, Brennan, Jonathan R.

Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model

Externí odkaz: http://arxiv.org/abs/2403.17299

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Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstruction

Autor: Li, Jiawei, Guo, Chunxu, Fu, Li, Fan, Lu, Chang, Edward F., Li, Yuanning

Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural recordings into speech using complex deep neural network (DNN) models

Externí odkaz: http://arxiv.org/abs/2310.04644

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Do self-supervised speech and language models extract similar representations as human brain?

Autor: Chen, Peili, He, Linyang, Fu, Li, Fan, Lu, Chang, Edward F., Li, Yuanning

Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given their distinct training modalities, it remains unclear whether they corre

Externí odkaz: http://arxiv.org/abs/2310.04645

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