How does the brain extract relevant visual features from the rich, dynamic visual input that typifies active exploration, and how does the neural representation of these features support visual navigation?
Yuchen Hou
(she/her/hers)
PhD Candidate
Psychological & Brain Sciences
University of California, Santa Barbara
Yuchen Hou is a PhD Candidate in Computer Science at UC Santa Barbara. She is interested in computational neuroscience and machine learning. Her research goal is to model the dynamics of brain functions by integrating knowledge from computer, cognitive, and neural science.
Prior to pursuing her PhD studies, she was an undergraduate research assistant in the Bionic Vision Lab with a BS degree in Psychological & Brain Sciences.
In her free time, she likes reading fiction books and watching action movies.
- 3205 BioEngineering
- yuchenhou@ucsb.edu
Honors & Awards
- Google Summer Internship (2026)
- Exceptional Academic Performance Award, PBS, UCSB (2022)
- Abdullah & Marjorie R. Nasser Memorial Scholarship Fund Award, PBS, UCSB (2022)
Education
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PhD in Computer Science, 2027 (expected)
University of California, Santa Barbara
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BS in Psychological & Brain Sciences, 2022
University of California, Santa Barbara
Project Lead
Project Affiliate
NeuroAI Models of the Visual System
Understanding the visual system in health and disease is a key issue for neuroscience and neuroengineering applications such as visual prostheses.
Publications
Beyond neural activity prediction: Probing latent representations in mouse V1 digital twins
We introduce a multi-level evaluation framework for digital twins of mouse V1 that links neural-prediction accuracy to probe decodability, latent-unit tuning, and hidden-population geometry.
Adriano Lima, Yuchen Hou, Michael Beyeler, Marius Schneider arXiv
Visual robustness and neural alignment in a shared foraging task: The Mouse vs. AI benchmark
We introduce Mouse vs. AI, a public benchmark suite that unifies visual robustness, embodied foraging behavior, and neural alignment by evaluating artificial agents and mice in the same naturalistic 3D task.
Marius Schneider, Joe S. Canzano, Yuchen Hou, Jing Peng, Anjali Deepu, Utsab Karan, Phu-Hoa Pham, Tran Chi Nguyen, Dao Sy Duy Minh, Phu Quy Nguyen Lam, Trung-Kiet Huynh, Simone Azeglio, Spencer LaVere Smith, Michael Beyeler arXiv
Predicting the temporal dynamics of prosthetic vision
We introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System.
Yuchen Hou, Laya Pullela, Jiaxin Su, Sriya Aluru, Shivani Sista, Xiankun Lu, Michael Beyeler IEEE EMBC ‘24
(Note: YH and LP contributed equally to this work.)
Axonal stimulation affects the linear summation of single-point perception in three Argus II users
We retrospectively analyzed phosphene shape data collected form three Argus II patients to investigate which neuroanatomical and stimulus parameters predict paired-phosphene appearance and whether phospehenes add up linearly.
Yuchen Hou, Devyani Nanduri, Jacob Granley, James D. Weiland, Michael Beyeler Journal of Neural Engineering
Multimodal deep learning model unveils behavioral dynamics of V1 activity in freely moving mice
We introduce a multimodal recurrent neural network that integrates gaze-contingent visual input with behavioral and temporal dynamics to explain V1 activity in freely moving mice.
Aiwen Xu, Yuchen Hou, Cristopher M. Niell, Michael Beyeler 37th Conference on Neural Information Processing Systems (NeurIPS) ‘23
