7 papers
A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity
Chengrui Li, Yunmiao Wang, Yule Wang +3
Low-rank recurrent neural networks (lrRNNs) are a class of models that uncover low-dimensional latent dynamics underlying neural population activity. Although their functional conn…
BehaviorVLM: Unified Finetuning-Free Behavioral Understanding with Vision-Language Reasoning
Jingyang Ke, Weihan Li, Amartya Pradhan +2
Understanding freely moving animal behavior is central to neuroscience, where pose estimation and behavioral understanding form the foundation for linking neural activity to natura…
Uncovering Semantic Selectivity of Latent Groups in Higher Visual Cortex with Mutual Information-Guided Diffusion
Yule Wang, Joseph Yu, Chengrui Li +2
Understanding how neural populations in higher visual areas encode object-centered visual information remains a central challenge in computational neuroscience. Prior works have in…
Learning When to Look: On-Demand Keypoint-Video Fusion for Animal Behavior Analysis
Weihan Li, Jingyang Ke, Yule Wang +2
Understanding animal behavior from video is essential for neuroscience research. Modern laboratories typically collect two complementary data streams: skeletal keypoints from pose…
Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes
Weihan Li, Yule Wang, Chengrui Li +1
Understanding and constructing brain communications that capture dynamic communications across multiple regions is fundamental to modern system neuroscience, yet current methods st…
A Revisit of Total Correlation in Disentangled Variational Auto-Encoder with Partial Disentanglement
Chengrui Li, Yunmiao Wang, Yule Wang +3
A fully disentangled variational auto-encoder (VAE) aims to identify disentangled latent components from observations. However, enforcing full independence between all latent compo…