2 citations · 5 across the 13 of their papers we have counts for
13 papers
Learning When Visual Context Matters for Mouse Behavior Analysis
Weihan Li, Jingyang Ke, Yule Wang +3
Understanding mouse behavior at scale is a foundational problem in neuroscience, with two core tasks: classifying frames into known behaviors and segmenting recordings into recurri…
A hitchhiker's guide to Poisson gradient estimation
Michael Ibrahim, Hanqi Zhao, Eli Sennesh +5
Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approache…
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…
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…
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…
Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models
Yule Wang, Chengrui Li, Weihan Li +1
Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, wh…
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 Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing
Chengrui Li, Weihan Li, Yule Wang +1
The partially observable generalized linear model (POGLM) is a powerful tool for understanding neural connectivity under the assumption of existing hidden neurons. With spike train…
Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
Weihan Li, Chengrui Li, Yule Wang +1
Studying the complex interactions between different brain regions is crucial in neuroscience. Various statistical methods have explored the latent communication across multiple bra…
Forward Divergence Based Variational Importance Sampling
Chengrui Li, Yule Wang, Weihan Li +1
Maximizing the log-likelihood is a crucial aspect of learning latent variable models, and variational inference (VI) stands as the commonly adopted method. However, VI can encounte…
One-hot Generalized Linear Model for Switching Brain State Discovery
Chengrui Li, Soon Ho Kim, Chris Rodgers +2
Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits. Inferred neural interactions from neural signals primarily reflect functiona…
Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
Yule Wang, Zijing Wu, Chengrui Li +1
In the field of behavior-related brain computation, it is necessary to align raw neural signals against the drastic domain shift among them. A foundational framework within neurosc…
Inverse Kernel Decomposition
Chengrui Li, Anqi Wu
The state-of-the-art dimensionality reduction approaches largely rely on complicated optimization procedures. On the other hand, closed-form approaches requiring merely eigen-decom…