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Title: Soft and Flexible Brain-Computer Interfaces, Presented by Jia Liu
Abstract: Understanding brain function through large-scale brain-computer interfaces (BCIs) is essential for deciphering neural dynamics, treating neurological disorders, and developing advanced neuroprosthetics. A major challenge in the field is to achieve simultaneous, large-scale, stable recording of neural activity, with single-cell resolution, millisecond precision, and cell-type specificity across three-dimensional (3D) brain tissue, throughout development, learning, and aging. In this talk, I will introduce a suite of soft and flexible bioelectronic technologies engineered to address this challenge in brain-computer interfaces. First, I will present tissue-like bioelectronics capable of tracking the activity of individual neurons in behaving animals throughout their adult lives. Then, I will discuss the electrochemical limitations of soft materials and our strategies to overcome them, establishing a scalable platform for large-scale, stable, and long-term brain mapping with a pathway toward human clinical translation. Next, I will discuss the creation of “cyborg organisms” by integrating stretchable, mesh-like electrode arrays into 2D sheets of stem/progenitor cells that undergo 2D-to-3D morphogenesis to form brain organoids or embryonic brains, enabling continuous 3D electrophysiological recording during development. I will then highlight how the brain’s dynamic nature—and the challenge of capturing neural changes over time—can be addressed by stable recordings enabled by flexible BCIs to decode intrinsic neural signal drift. These platforms support long-term, adaptive neural decoding and facilitate integration with neuromorphic algorithms for real-time interpretation of intrinsic neural dynamics. Building on this, I will introduce DriftNet, a deep neural network framework inspired by neural dynamics. DriftNet mitigates catastrophic forgetting, outperforming conventional and state-of-the-art lifelong learning models, and equips large language models with cost-effective, lifelong learning capabilities. Finally, I will present our latest efforts integrating 3D single-cell spatial transcriptomics, electrophysiology, and agentic AI to map brain activity with cell-type specificity. I will conclude by outlining a future vision in which soft and flexible electronics, spatial omics, and AI agents converge to construct a comprehensive brain cell functional atlas, transforming next-generation BCI applications.
Bio: Professor Liu received his Ph.D. in Chemistry from Harvard University in 2014 and completed postdoctoral training at Stanford University in 2018. He joined Harvard School of Engineering and Applied Sciences as an Assistant Professor in 2019. At Harvard, his lab develops tissue-integrated intelligent bioelectronic systems that merge flexible and soft electronics with living tissues, and combine multimodal in situ characterization with deep learning and agentic AI to decode and control biological processes. His research spans tissue-like bioelectronics and cyborg organoids, as well as AI-driven multimodal analysis and tissue functional control, to understand neural dynamics, organ function, and disease mechanisms. Professor Liu has pioneered new paradigms in bioelectronics, establishing foundations for soft electronic materials, nanoarchitectures for tissue-like electronics, and AI-integrated bioelectronic systems. His work was recognized as a milestone in bioelectronics by Science (2013, 2017) and was selected as one of the Top 10 World-Changing Ideas and the Most Notable Chemistry Research (2015). He has received numerous honors, including NSF CAREER, MIT Technology Review’s “Innovators Under 35” (Global List), the AFOSR Young Investigator Program (YIP) Award, the NIH/NIDDK Catalyst Award (from the NIH Director’s Pioneer Award Program), the William F. Milton Award, and the Aramont Award for Emerging Science Research Fellowship. Professor Liu is also a co-founder and scientific advisor of several deep-tech startups that translate his lab’s innovations into practice, including Axoft, Elastro, MorphMind (AIScientist), and NanoRythmics.
Title: From Neurotechnology Discovery to Clinical-Scale Systems, Presented by Carolina Mora Lopez
Abstract: Neurotechnology is progressing from experimental research tools toward translational and clinical systems for recording, decoding, and modulating neural activity. This transition requires not only advances in neural interfaces, electronics, packaging, and data processing, but also solutions for long-term reliability, biocompatibility, power safety, manufacturability, regulation, and cost. This presentation discusses how shared R&D platforms and collaborations can help address these challenges. Using high-density CMOS neural probes as an example, it examines how diverse scientific and clinical requirements can be translated into common technology roadmaps, enabling broadly useful neurotechnology platforms while preserving opportunities for application-specific innovation.
Bio: Carolina Mora Lopez received her Ph.D. degree in Electrical Engineering in 2012 from the KU Leuven, Belgium, in collaboration with imec, Belgium. From 2012 to 2018, she worked at imec as a researcher and analog designer focused on interfaces for neural-sensing applications. During this time, she was the lead analog designer and project leader of the Neuropixels development projects, which resulted in the conception and fabrication of the Neuropixels 1.0 and 2.0 neural probes. She is currently the Scientific Director of Neurotech in imec, overseeing the development of circuits and technologies for electrophysiology, neuroprosthetics, and BMI. Her research interests include analog and mixed-signal circuit design for sensor, bioelectronic, and neural interfaces. Carolina is a senior IEEE member and serves on the technical program committee of the VLSI symposium.
Title: Human Single-Neuron Recordings: Data Acquisition, Processing, and Analytics, Presented by Shuo Wang
Abstract: Human single-neuron recordings provide a rare and powerful window into the neural basis of cognition, offering unmatched spatial and temporal resolution at the level of individual cells. In this talk, I will provide an end-to-end overview of how these data are obtained and analyzed in clinical intracranial recording settings. I will first describe the experimental and technical considerations involved in data acquisition, including electrode implantation, task design, and synchronization of neural and behavioral measurements. I will then discuss key steps in data processing, focusing on spike detection and spike sorting, as well as quality control and validation of single-unit isolation. Building on this foundation, I will present a case study from a human visual search paradigm to illustrate analytical approaches for linking single-neuron activity to behavior. Specifically, I will demonstrate how firing-rate dynamics, temporal coding, and population-level analyses can be used to characterize the neural mechanisms underlying visual attention, target selection, and memory-guided search. Together, this talk aims to provide both a practical introduction to human single-neuron methodologies and a conceptual framework for leveraging these data to address fundamental questions about how the human brain encodes, selects, and remembers information.
Bio: Dr. Shuo Wang is an Associate Professor of Radiology, Neurosurgery, and Biomedical Engineering at Washington University in St. Louis. He received his Ph.D. from Caltech in 2014 and completed postdoctoral training at Caltech and Princeton University in 2017. His research integrates multimodal, advanced measurement techniques with sophisticated computational approaches to elucidate the neural mechanisms and computations underlying face and object processing, visual attention, emotion, and memory. The overarching questions guiding his work focus on how the brain recognizes faces and objects, discerns environmental significance, and translates visual information into memory. Using a multimodal approach, he investigates these questions at both the microscopic level—through single-neuron recordings in humans and macaques—and the macroscopic level, employing fMRI, EEG, and intracranial EEG. These experimental methods are complemented by advanced computational techniques designed to handle complex, large-scale datasets. His research also includes characterizing individual differences in cognitive processes, particularly to understand how these processes are disrupted in autism. His research has been recognized with an NSF CAREER Award, a DoD Young Investigator Award, the Dana Clinical Neuroscience Award, a Powe Award, and SANS Mid Career Award.