This page was last updated on September 30, 2024. My Google Scholar page is much more up to date!
* indicates first author
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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Yenho Chen*, Noga Mudrik, Kyle A Johnsen, Sankaraleengam Alagapan, Adam S Charles, Christopher J Rozell
Advances in Neural Information Processing Systems (2024). (Acceptance Rate 25.8%)
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LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Noga Mudrik*, Eva Yezerets, Yenho Chen, Christopher J Rozell, Adam S Charles
Transactions on Machine Learning Research (2024). Accepted.
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MANGO: Disentangled Image Transformation Manifolds with Grouped Operators
Brighton Ancelin, Yenho Chen, Alex Saad-Falcon, Peimeng Guan, Chiraag Kaushik, Belen Martin-Urcelay, Nakul Singh
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2025). Submitted.
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AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking.
Yuheng Li*, Tianyu Luan, Yizhou Wu, Shaoyan Pan, Yenho Chen, Xiaofeng Yang
European Conference on Computer Vision (ECCV) 2024. Accepted.
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Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Noga Mudrik*, Yenho Chen*, Eva Yezerets, Christopher J Rozell, and Adam S Charles
Journal of Machine Learning Research 25.59 (2024): 1-44.
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Working memory and reward increase the accuracy of animal location encoding in the medial prefrontal cortex
Xiaoyu Ma*, Charles Zheng, Yenho Chen, Francisco Pereira, and Zheng Li
Cerebral Cortex, Feb 2023.
The ability to perceive spatial environments and locate oneself during navigation is crucial for the survival of animals. Mounting evidence suggests a role of the medial prefrontal cortex (mPFC) in spatially related behaviors. However, the properties of mPFC spatial encoding and how it is influenced by animal behavior are poorly defined. Here, we train the mice to perform 3 tasks differing in working memory and reward-seeking: a delayed non-match to place (DNMTP) task, a passive alternation (PA) task, and a free-running task. Single-unit recording in the mPFC shows that although individual mPFC neurons exhibit spatially selective firing, they do not reliably represent the animal location. The population activity of mPFC neurons predicts the animal location. Notably, the population coding of animal locations by the mPFC is modulated by animal behavior in that the coding accuracy is higher in tasks involved in working memory and reward-seeking. This study reveals an approach whereby the mPFC encodes spatial positions and the behavioral variables affecting it.
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Testing for context-dependent changes in neural encoding in naturalistic experiments
Yenho Chen*, Carl W Harris, Xiaoyu Ma, Zheng Li, Francisco Pereira, and Charles Y Zheng
arXiv preprint arXiv:2211.09295, Feb 2022.
Conference Abstracts
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Decomposed linear dynamical systems for understanding neural signals
Yenho Chen, Noga Mudrik, Eva Yezerets, Christopher Rozell, and Adam Charles
Collaborative Research in Computational Neuroscience (CRCNS), October 2022, Atlanta, GA. Poster 110
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Decomposed linear dynamical systems for C. elegans functional connectivity
Eva Yezerets, Noga Mudrik, Yenho Chen, Christopher Rozell, and Adam Charles
Computational and Systems Neuroscience (COSYNE), March 2023, Montreal, Canada. Poster II-132