← Search

Jiashuang Huang

4 accepted papers

2026

Spike-based Digital Brain: a novel fundamental model for brain activity analysis

ICLR 2026poster

Modeling the temporal dynamics of the human brain remains a core challenge in computational neuroscience and artificial intelligence. Traditional methods often ignore the biological spike characteristics of brain activity and find it difficult to reveal the dynamic dependencies and causal interactio…

Cited by 0SourcecodeScholar
2025

SCNN: Spike Coupling Neural Network for Multimodal Brain Network Analysis

ICASSP 2025accepted

Structure-function coupling (SC-FC coupling) refers to the correlation between the physical layout of structural connectivity (SC) in the brain and the activity patterns of functional connectivity (FC). However, most existing SC-FC coupling analysis methods lack system-level integration and overlook…

Cited by 0SourceScholar
2025

SCNNs: Spike-based Coupling Neural Networks for Understanding Structural-Functional Relationships in the Human Brain

IJCAI 2025

Structural-functional coupling (SC-FC coupling) offers an effective approach for analyzing structural-functional relationships, capable of revealing the dependency of functional activity on the underlying white matter architecture. However, extant SC-FC coupling analysis methods primarily center on

Cited by 0SourcePDFScholar
2025

SUFT: Sparse and Uncertain Fusion Transformers for Multi-Atlas Brain Network Analysis

ICASSP 2025accepted

The existing multi-atlas brain network analysis methods rely on some simple fusion methods (i.e., add and concatenation) and do not consider the information redundancy caused by increased brain regions. To improve upon these, we propose the Sparse and Uncertain Fusion Transformers (SUFT) for multi-a…

Cited by 0SourceScholar