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Plug-and-Play Stability for Intracortical Brain-computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication

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arXiv 2023年

作者： Fan, Chaofei Hahn, Nick Kamdar, Foram Avansino, Donald Wilson, Guy H. Hochberg, Leigh Shenoy, Krishna V. Henderson, Jaimie M. Willett, Francis R. Bio-X Program Stanford University United States Department of Neuroscience Stanford University United States Department of Neurosurgery Stanford University United States Department of Neurobiology Stanford University United States Department of Bioengineering Stanford University United States Department of Computer Science Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States Howard Hughes Medical Institute Stanford University United States Department of Electrical Engineering Stanford University United States School of Engineering Carney Institute for Brain Science Brown University United States VA RR&D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School United States

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain computer interface

Optogenetic Neural Probes: Fiberless, High-Density, Artifact-Free Neuromodulation : (Invited)

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Optogenetic Neural Probes: Fiberless, High-Density, Artifact...

International Electron Devices Meeting (IEDM)

作者： Eunah Ko Kanghwan Kim Mihály Vöröslakos Sungjin Oh György Buzsáki Kensall D. Wise Euisk Yoon Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Center for BioMicrosystems Brain Science Institute Korea Institute of Science and Technology Seoul Korea Neuroscience Institute Langone Medical Center New York University New York NY USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Center for Nanomedicine Institute for Basic Science (IBS) and Graduate Program of Nano Biomedical Engineering (Nano BME) Advanced Science Institute Yonsei University Seoul Korea

ISBN: (纸本)9781665489607

Evolution of “Michigan Probes” has enabled unraveling the connectivity of neurons at cellular resolution and their dynamic interaction within and across brain areas that underlie behavioral and cognitive functions. Recently, micro-LEDs were monolithically integrated for optogenetic neuromodulation at scale without requiring external optical fiber connection. The ‘hectoSTAR’ micro-LED optoelectrode features 256 recording electrodes and 128 stimulation micro-LEDs in four silicon micro-needle shanks, covering a large volume with 1.3-mm × 0.9-mm cross-sectional area located as deep as 6 mm inside the brain. Technical challenges to overcome in scaling of optogenetic probes will be discussed, followed by future direction.

关键词： Optical fibers Electrodes Brain Optogenetics Silicon Recording Optical fiber devices

Giant Deep-Ultraviolet Circular Dichroism in Synthetic Architectures of Ordered Carbon Nanotubes

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Giant Deep-Ultraviolet Circular Dichroism in Synthetic Archi...

Conference on Lasers and Electro-Optics (CLEO)

作者： Jacques Doumani Minhan Lou Oliver Dewey Nina Hong Jichao Fan Andrey Baydin Matteo Pasquali Yohei Yomogida Kazuhiro Yanagi Riichiro Saito Junichiro Kono Weilu Gao Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA Carbon Hub Rice University Houston TX USA Department of Chemical and Biomolecular Engineering Rice University Houston TX USA J.A. Woollam Co. Lincoln NE USA The Smalley-Curl Institute Rice University Houston TX USA Department of Chemistry Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University Houston TX USA

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Carbon nanotubes Chiral media Dichroism Graphene Optical activity Quantum information

Meta-optic Accelerators for Object Classifiers

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arXiv 2022年

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

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arXiv 2021年

作者： Jo, Jeonghee Kwak, Bumju Lee, Byunghan Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Recommendation Team Kakao Corporation Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Department of Electrical and Computer Engineering Seoul National University

A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational chemistry, several studies have focused on how to predict molecular properties based on molecular configurations. Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that all heterogeneous molecular features, such as atomic charge, bond length, or other geometric features always contribute equivalently to the target prediction, regardless of the task type. In this study, we propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target. In addition, we introduce a discrete branch to learn single atom features without local aggregation, apart from message-passing steps. We verify that this novel structure can improve the model performance with faster convergence in most targets. The proposed model outperforms other recent models with sparser representations. Our experimental results indicate that in the chemical property prediction tasks, the diverse chemical nature of targets should be carefully considered for both model performance and generalizability. © 2021, CC BY.

关键词： Deep learning

Discrete frequency infrared vibrational circular dichroism spectroscopy & imaging

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Discrete frequency infrared vibrational circular dichroism s...

Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life sciences 2021

作者： Phal, Yamuna Yeh, Kevin Bhargava, Rohit Department of Electrical and Computer Engineering University of Illinois at Urbana Champaign 306 N. Wright St UrbanaIL61801 United States Departments of Bioengineering Chemistry Chemical and Biomolecular Engineering Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Cancer Center at Illinois University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign 405 N. Mathews Ave UrbanaIL61801 United States

Vibrational Circular Dichroism (VCD) arises from chiroptical phenomena in biological samples. We demonstrate VCD imaging, a novel microscopy technique that can provide molecular- and spatially-specific chirality mappi... 详细信息

ISBN: (纸本)9781557528209

关键词： Dichroism

Terahertz Time-Domain Spectroscopy for Probing DC Conductivity of Single-Layer Graphene

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Terahertz Time-Domain Spectroscopy for Probing DC Conductivi...

Western New York Image Processing Workshop (WNYIPW)

作者： Debamitra Chakraborty Declan Walden Jing Cheng Julia Wallace Kiryl A. Niherysh Andrei V. Felsharuk Donats Erts Ivan Komissarov Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics Rochester New York USA Department of Physics and Astronomy University of Rochester Rochester New York USA Department of Biomedical Engineering University of Rochester Rochester New York USA Institute of Chemical Physics University of Latvia Riga Latvia Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

We report preliminary results of extracting electrical parameters like DC conductivity, mobility, and carrier concentration of single-layer graphene using Terahertz time-domain spectroscopy (THz-TDS) in transmission geometry. We utilize a simple Drude model and its extension, the Drude-Smith model, capable of accommodating charge carrier scattering by optical and acoustic phonons to describe the complex conductivity in graphene. We observed that a significant area of the sample scanned followed the Drude-Smith behavior, while at a few locations, the simple Drude behavior prevailed. We identified possible reasons that might contribute to this observation, like defects, the presence of polymer residue on the sample, or the sample strain. This research sheds new light on our understanding of graphene's electrical properties and lays the foundation for future investigations addressing defects and contaminants in graphene samples.

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Multi-core synthesis and maximum satisfiability applied to optimal sizing of solar photovoltaic systems

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TechRxiv

TechRxiv 2021年

作者： Trindade, Alessandro Galvão, Edilson Cordeiro, Lucas The Post-graduate Program in Electrical Engineering Manaus Brazil Department of Electricity Federal University of Amazonas Manaus Brazil School of Computer Science The University of Manchester United Kingdom

Annual global energy consumption growth is around 1.3% with forecasts until 2040. Photovoltaic systems became a suitable alternative to nuclear and fossil energy generation. In order to support this technology’s dissemination, we develop and evaluate an automated formal synthesis approach that assists in decision-making for off-grid systems. Our proposed approach, called PVz, is based on a variant of the counterexample-guided inductive synthesis;it has a multi-core feature, which can obtain the optimal sizing of photovoltaic systems focusing on Life Cycle Cost analysis. Given the electrical needs of a home, we seek a set of electrical equipment with the best possible combination of devices that meet the specified requirements. We calculate all costs related to maintenance over 20 years. The results presented are based on seven case studies;some of them are real ones from the Amazon region in Brazil. The same case studies were solved by a commercial optimization tool. Our technique and the commercial tool results were validated with popular simulation software to perform a fair comparison. Furthermore, we analyze some topics such as run-time, optimal solution, and configuration of the resulting systems. We claim that our technique is advantageous compared to the existing approaches in the literature. © 2021, CC BY-NC-SA.

关键词： Decision making

Graphene calorimetric single-photon detector

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arXiv 2024年

作者： Huang, Bevin Arnault, Ethan G. Jung, Woochan Fried, Caleb Russell, B. Jordan Watanabe, Kenji Taniguchi, Takashi Henriksen, Erik A. Englund, Dirk Lee, Gil-Ho Fong, Kin Chung Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Pohang University of Science and Technology Pohang790-784 Korea Republic of Department of Physics Washington University in St. Louis St. LouisMO United States Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan RTX BBN Technologies Quantum Engineering and Computing Group CambridgeMA02138 United States

Single photon detectors (SPDs) [1, 2] are essential technology in quantum science, quantum network, biology, and advanced imaging [3-5]. To detect the small quantum of energy carried in a photon, conventional SPDs rely on energy excitation across either a semiconductor bandgap or superconducting gap. While the energy gap suppresses the false-positive error, it also sets an energy scale that can limit the detection efficiency of lower energy photons and spectral bandwidth of the SPD [6-8]. Here, we demonstrate an orthogonal approach to detect single near-infrared photons using graphene calorimeters [9-13]. By exploiting the extremely low heat capacity of the pseudo-relativistic electrons in graphene near its charge neutrality point [14], we observe an electron temperature rise up to ∼2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count © 2024, CC BY.

关键词： Particle beams