Mingjun Zhang

Bio

Professor and National Distinguished Scholar, Tsinghua University, P. R. China. Chief scientist for national key R&D project on Brain-Machine Intelligence fusion through brain-machine interfaces. D.Sc. ESE (Washington University in St. Louis), PhD Industrial Automation (Zhejiang University), MS BioE & MS EE (Stanford University), MS CS (University of Missouri-Rolla), BS/MS ME (Zhejiang University). After working as an R&D engineer (Agilent Technologies, 2001-2007), associate professor (tenured, University of Tennessee – Knoxville, 2008-2013), full professor (tenured, The Ohio State University, 2014-2019), he joined Tsinghua University as a tenured full professor in 2020.

As a corresponding or lead co-author, he published research papers in Cell Device, Nature Nanotechnology, Nature Communications, Science Advances, PNAS, IEEE Transactions. Research results from his lab has been highlighted by Science, Nature, National Science Foundation of USA, National Science Foundation of China, AAAS, Biomedical Engineering Society. Nature Nanotechnology highlighted his research “paves the way for a new front in peptide optics, News & Views, Jan. 2016.”

Received 20+ prestigious research grants as PI from NSF, DoD, NIH of USA, and NSF-China, Department of Science & Technology of China. Received early career award (IEEE Robotics & Automation Society), Young Investigator Award (Office of Naval Research, USA), Innovation Award (Agilent Technologies).

Topic and Abstract

Advances in Minimally Invasive Brain-Machine Interfaces: Principles, Technologies, and Frontier Applications

Abstract: Minimally invasive brain–machine interfaces (BMIs) are poised to revolutionize the diagnosis and treatment of central nervous system disorders, while opening new horizons in human–machine symbiosis. This tutorial will provide a concise yet comprehensive overview of the scientific foundations and engineering innovations that underpin minimally invasive BMIs, guiding participants from basic neural interfacing principles all the way to real-world applications and future outlooks.

Part I: Foundations of Neural Interfacing. Covers core brain sciences from an engineering viewpoint: brain anatomy and neuron biology, the physical, chemical, and biological interactions at electrode–tissue interfaces, and system-theoretic frameworks for micro-/nano-scale neural modulation.

Part II: Engineering Principles and Technologies. Details state-of-the-art materials and device architectures, including biocompatible micro/nano-electrodes, self-expanding multimodal probes, and nanotransducers. Reviews fabrication methods (e.g., MEMS, bio-inspired coatings), hardware/software integration, and advanced signal-processing algorithms such as dimensionality reduction, spike sorting, and real-time decoding.

Part III: Applications, Challenges, and Outlook. Surveys clinical and research deployments of minimally invasive BMIs, highlights validation strategies and ethical considerations, and examines the role of animal models and international standards. Concludes with emerging opportunities for wireless micro/nano-BMI systems and interdisciplinary integration to address future neuroscience and AI challenges.

Xinjian Li

Bio

Dr. Xinjian Li is a Principal Investigator in Neuroscience at the Zhejiang University School of Medicine, where he leads pioneering research on the neural mechanisms of vocal communication, motor control, spatial navigation, and related neurological disorders. His work employs an interdisciplinary approach, combining advanced methodologies such as brain-machine interfaces, single-cell calcium imaging, and cross-species behavioral analysis in non-human primates (marmosets, macaques) and rodents (mice, rats). A prolific researcher, Dr. Li has authored ~30 high-impact publications in prestigious journals including Nature, Cell, Nature Neuroscience, National Science Review, Advanced Materials, Advanced Science, Nature Communications, eLife, and Communications Biology.

Dr. li earned his Ph.D. from the Institute of Neuroscience, Chinese Academy of Sciences, followed by thee-rounds of postdoctoral training at The Hong Kong Polytechnic University, Johns Hopkins University, and the National Institutes of Health (NIH), solidifying his expertise in translational neuroscience.

Topic and Abstract

Deciphering Neural Mechanisms of Marmoset Vocal Communication Using a Miniaturized Wireless Large-Scale Brain-Machine Interface

Abstract: Human speech and animal vocalizations play a crucial role in social interaction and survival. However, traditional studies have been limited by head-fixed or tethered setups, restricting recordings to small, isolated brain regions. In this tutorial, we present a customized miniaturized wireless brain-machine interface (BMI) that enables large-scale neural recordings in freely behaving marmosets, offering unprecedented insights into vocal communication mechanisms.

Part I: Neural Mechanisms of Acoustic Perception. Using brain-shaped μECoG arrays implanted in the frontal cortex and temporal lobe, we recorded neural activity in response to multi-layered acoustic stimuli. This approach allowed us to dissect: acoustic-driven neural responses, brain-wide information flow (feedforward and feedback signaling), dynamic neural communication across cortical regions.

Part II: Neural Dynamics During Vocal and Non-Vocal Behaviors, we extended our recordings to freely behaving marmosets engaged in naturalistic scenarios, including: vocal communication (social calls), fear-conditioned responses, innate behaviors (e.g., drinking). Our system successfully decoded distinct neural signatures associated with different behavioral phases, revealing how distributed brain networks coordinate complex actions.

Part III: Future Applications of Next-Generation Wireless BMI. we discuss potential advancements of this technology, including: high-density, multi-region neural mapping, closed-loop neuromodulation for behavioral control, translational applications in neuropsychiatric disorders. This work establishes a paradigm shift in neural recording techniques, enabling naturalistic, large-scale brain monitoring to uncover the neural basis of vocal communication and beyond.

Jinhong Luo

Bio

Dr. Jinhong Luo is a professor in the School of Life Sciences at Central China Normal University. His research focuses on the brain mechanisms of sensorimotor behavior of bats, the only mammalian lineage capable of powered flight, and most rely on a biosonar system for spatial orientation and foraging.

He has authored over 40 publications in journals such as PNAS, PLOS Biology, and Trends in Neurosciences. He is currently an elected member of the Animal Behavior Society of China and the Bioacoustics Society of China, and serves as the vice director of the Wuhan Zoological Society.

Topic and Abstract

Neural Basis of Ultrafast Sensorimotor Behaviors in Echolocating Bats: A Potential Model for Brain-Machine Interface Research

Abstract: Bats are among the most successful mammalian groups: they occupy diverse ecological niches, are distributed in virtually every corner of the terrestrial landscape, and account for ~1/5 of the total mammalian species. Remarkably, bats are the only mammals capable of powered flight, and most of them navigate in the dark using a biosonar system termed echolocation. Decades of research have shown that echolocation in bats is a highly precise, ultrafast, and adaptive (intelligent) system. Yet, we are only beginning to understand the neuronal and circuit mechanisms for this remarkable sensorimotor behavior.

In this presentation, I will first provide an overview of recent progress in understanding the neural mechanisms of bat echolocation, focusing on studies involving neuronal recordings in behaving bats and new technologies for bats, such as chemogenetics and two-photon imaging.

Next, I will summarize the brain regions and circuits implicated in echolocation call production and control, and identify candidate targets for manipulating echolocation behavior through brain-machine interfaces (BMI).

I will conclude the presentation by suggesting frontier topics in which bats may contribute to advancing the fundamental and application research of BMI.