Biography

Dr. Xiaoxuan Jia is an Associate Professor at the School of Life Sciences at Tsinghua University and a Principal Investigator at the IDG/McGovern Institute for Brain Research. She received her B.S. in biotechnology and biological sciences from Tsinghua University and her Ph.D. in neuroscience from Albert Einstein College of Medicine, New York, in 2012. After postdoctoral training at MIT, she joined the Allen Institute as a Senior Scientist and Project Lead in 2016. She joined Tsinghua University in 2022 as an Associate Professor. Her research focuses on understanding how visual information is efficiently compressed, represented, and dynamically routed in the hierarchical and recurrent brain networks through a multidisciplinary approach that combines large-scale electrophysiology, machine learning, and theoretical modeling.

Talk Title: Dynamic Signal Propagation in the Visual Cortex

The brain is a complex dynamical system whose computations emerge from recurrent interactions among neurons across multiple spatial and temporal scales. A central question is how cortical networks route information carried by spikes as sensory input, internal state, and experience change. Large-scale Neuropixels recordings make these questions experimentally accessible by simultaneously recording the spiking activity of neuronal populations across multiple cortical areas. By inferring directed interactions from millisecond-scale spike timing, we can investigate signal-transmission networks at single-neuron resolution and examine how their organization relates to visual coding. In this talk, I will first describe the functional architecture of the mouse visual system, focusing on its hierarchical organization and the spatial and temporal separation of feedforward and recurrent processing. I will then examine how stimulus features, behavioral state, and visual experience dynamically shape signal transmission within this network, combining network analysis and computational modeling to explore the consequences for visual coding. Together, these findings suggest that the relatively stable anatomical connectome defines the possible routes of communication, while dynamic signal routing supports efficient and adaptive information processing within the same network.