Shanglin Zhou-INSTITUTE FOR TRANSLATIONAL BRAIN RESEARCH

Shanglin Zhou

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Shanglin Zhou Principal Investigator

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Computational and Systems Neuroscience

Shanglin Zhou is a Principal Investigator at the Institute for Translational Brain Research， Fudan University. He obtained his Bachelor's degree in Pharmacy from West China School of Pharmacy, Sichuan University in 2008, and earned his Ph.D. in Neuropharmacology from the Shanghai Institute of Materia Medica, Chinese Academy of Sciences in 2014. He subsequently pursued interdisciplinary training in computational neuroscience and systems neuroscience at Fudan University (2014-2016) and the University of California, Los Angeles (UCLA) (2017-2023). In September 2023, Dr. Zhou joined Institute for Translational Brain Research，where he established the Computational and Systems Neuroscience Laboratory. The laboratory employs multidisciplinary approaches including human psychophysical experiments, animal behavioral studies, large-scale neural activity recording, and computational modeling. Its long-term research focus centers on investigating the neural and computational mechanisms underlying higher cognitive functions such as time cognition, spatial cognition, working memory, and flexible computations.

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We live in a four-dimensional spacetime, where the brain's perception of temporal and spatial information in the physical world is essential for survival. Decades of research have provided profound insights into how the brain encodes and utilizes spatial information, yet our understanding of time cognition remains limited. In reality, the ability to perceive time and generate timely behaviors is one of the brain's most fundamental cognitive functions. We rely on temporal perception to accomplish daily tasks—from estimating elapsed time since an event, anticipating and reacting to a tennis serve, to precisely controlling rhythmic patterns in musical performance. One major focus of our laboratory is to investigate the neural and computational mechanisms underlying the brain's perception, encoding, and utilization of temporal information.

Many daily functions—such as language processing—require the brain to process sequentially ordered information, known as temporal sequences. These sequences can be categorized by their information carriers, such as syllables in speech. Among all temporal sequences, spatiotemporal information—where spatial features carry temporal sequences—holds particular significance. Examples include unlocking a phone through gesture patterns, memorizing event timelines with spatial contexts, or handwriting digits, all demanding integrated processing of temporal and spatial information. Despite its critical role, the neural mechanisms underlying the joint encoding and utilization of spatiotemporal information remain poorly understood. Thus, our second research direction focuses on elucidating how the brain jointly encodes, stores, and decodes temporal and spatial information.

Compared to current artificial intelligence systems, the human brain exhibits superior flexible computational capabilities, manifested in multi-task learning, seamless task-switching, cognitive invariance under transformations (rotation, translation, scaling etc.), and the corresponding adaptive generalization. A third major direction of our laboratory involves exploring the neural and computational mechanisms enabling such efficient and flexible computations.

Specific research directions include but are not limited to:

Neural and computational mechanisms of temporal and spatial information representation, storage, and decoding in neural networks.
Mechanisms underlying spatiotemporal transformations and transformation-invariance in sensorimotor behaviors and working memory.
Neural and computational principles governing the learning and generalization for multi-tasks and multi-transformations.
Development of interdisciplinary tools for quantitative characterization and modeling of neuroscientific processes.

To achieve these goals, our laboratory employs an integrated experimental-theoretical approach combining: