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Seminar: Professor Xiao-Jing Wang
March 29 @ 4:00 pm - 5:30 pm
What does it mean to build a large-scale brain circuit model?
Wednesday 29 March @ 4 pm – 5:30 pm (Le Gros Clark Lecture Theatre, DPAG)
We are honoured to be hosting Xiao-Jing Wang from the New York University and New York University Shanghai, will talk about his research at the Cortex Club on Wednesday, 29th of March.
“Research in my group aims at understanding dynamical behavior and function of neural circuits. Using theoretical and modeling approaches, in close collaboration with experimentalists, we investigate the neural mechanisms and computational principles of cognitive processes, such as decision-making (how we make a choice among multiple options) and working memory (how our brain holds and manipulates information “online” in the absence of sensory stimulation).”
In this talk, I will first promote the idea of variations on the theme of a canonical local circuit, contrasting sensory microcircuits dedicated to coding and processing stimuli from senses with “cognitive-type” microcircuits capable of working memory and decision-making. This line of research has led us to study multi-region brain systems based on mesoscopic connectome and physiological experiments. We have developed large-scale cortex modeling of macaque monkey and mouse. By taking into account variations across cortical areas, the model naturally gives rise to a hierarchy of timescalesand, endowed with a laminar structure of the cortex, itcaptures frequency-dependent interactions betweenbottom-up and top-down processes. Moreover, in a complex brain system, routing of information between areas must be flexibly gated according to behavioral demands. We proposed such a gating mechanism with a disinhibitory circuit motif implemented by threesubtypes of (PV+, SOM+ and VIP+) inhibitory neurons, and I will report a recent finding that the relative distribution of these three interneuron classesvaries markedly across the whole mouse cortex. Finally, I will show preliminary results on distributed working memory representation. Circuit modeling across levels, combined with training multi-module recurrent networks, represents a promising approach to elucidate high-dimensional dynamics and functions of the global brain.