Dongsheng Xiao

My research has centred on elucidating the neural mechanisms underlying complex sensorimotor functions and motor learning through an interdisciplinary blend of cutting‐edge neuroscience and advanced computational methods. Early in my career, I pioneered the use of intraoperative microelectrode recording and stimulation to map the basal ganglia and thalamus, enabling precise modulation of motor functions via deep brain stimulation. Driven by my research interests, I embarked on a PhD journey in Prof. Tim Murphy’s lab at the University of British Columbia (UBC) where I was honoured with the Scholarships from the International Alliance of Translational Neuroscience. Prof Tim Murphy’s lab develops cutting-edge neurophysiological techniques to probe the complex information flow in the brain during sensorimotor processing. I co-led the development of intact skull chronic windows for mesoscopic wide‐field imaging in awake mice, a now widely adopted technique described in Silasi, Xiao et al., 2016 (co-first author, 241 citations). I also pioneered methodologies for mapping functional connectivity between cortical mesoscopic networks and subcortical single spiking neurons (Xiao et al., 2017. 151 citations). My research integrates artificial intelligence and computer vision to automate the exploratory analysis of the rich neural and behavioural video datasets. I have combined chronic, simultaneous wide‐field imaging, multi‐site electrophysiology using the Mesotrode (Xiao et al., 2023), and advanced machine learning tools (e.g., MesoNet, Xiao et al., 2021, Nat. Commun.) to capture and quantify large‐scale neuronal spatiotemporal patterns associated with a specific motor act, such as self-initiated running, reaching and orofacial movements. I also co-developed a 3D virtual mouse model that translates 2D behavioural videos into a 3D model space, enabling more detailed analysis of mouse behaviours, and established a standardised behavioural framework to disentangle movement dynamics from unrelated factors. This work was featured on the cover of Nature Methods (Bolaños, Xiao et al., 2021. co-first author). I contributed to the development of real-time systems for selectively tracking mouse body movements. This work paves the way for advanced "closed loop" brain-computer interfaces, facilitating understanding of the neural basis of behavioural control (Forys, Xiao et al., 2018, 2020. co-first author). My recent work in the Balbi lab at the University of Queensland on continuous auditory feedback demonstrates that real-time, movement-coded auditory cues can significantly accelerate fine motor skill learning in mice (Xiao and Balbi, eNeuro), exemplifying how augmented sensory input can promote motor performance. Similarly, my contributions to real-time EEG-based asynchronous error prediction in human–robot interaction using machine learning (Xiao et al., under revision) and the development of MesoGAN—a Generative Adversarial Network (GAN) framework that generates realistic behavioural videos from neural decoding of wide-field cortical calcium dynamics (Xiao et al., under revision)— highlighting my commitment to bridging neuroscience with adaptive robotics and AI.