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Nano functional neural interfaces

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Nano Research 2018年第10期11卷 5065-5106页

作者： Yongchen Wang Hanlin Zhu Huiran Yang Aaron D. Argall Lan Luan Chong Xie Liang Guo Department of Biomedical Engineering The Ohio State University Columbus 43210 USA Department of Biomedical Engineering The University of Texas at Austin Austin 78712 USA Department of Electrical and Computer Engineering The Ohio State University Columbus 43210 USA Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing 211816 China Biomedical Sciences Graduate Program The Ohio State University Columbus 43210 USA Department of Neuroscience The Ohio State University Columbus 43210 USA

Engineered functional neural interfaces （fNIs） serve as essential abiotic-biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms, fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.

关键词： neural interface neurotechnology nanoelectrode nanomaterial neural recording neural stimulation

Human brain mapping with multi-thousand channel PtNRGrids resolves novel spatiotemporal dynamics

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arXiv 2021年

作者： Tchoe, Youngbin Bourhis, Andrew M. Cleary, Daniel R. Stedelin, Brittany Lee, Jihwan Tonsfeldt, Karen J. Brown, Erik C. Siler, Dominic Paulk, Angelique C. Yang, Jimmy C. Oh, Hongseok Ro, Yun Goo Choi, Woojin Lee, Keundong Russman, Samantha Ganji, Mehran Galton, Ian Ben-Haim, Sharona Raslan, Ahmed M. Dayeh, Shadi A. Integrated Electronics and Biointerfaces Laboratory Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of Neurological Surgery University of California San Diego San DiegoCA92093 United States Department of Neurological Surgery Oregon Health & Science University Mail Code CH8N 3303 SW Bond Avenue PortlandOR97239-3098 United States Department of Obstetrics Gynecology and Reproductive Sciences Center for Reproductive Science and Medicine University of California San Diego San DiegoCA92093 United States Department of Neurology Massachusetts General Hospital BostonMA02114 United States Department of Neurosurgery Massachusetts General Hospital BostonMA02114 United States Graduate Program of Materials Science and Engineering University of California San Diego San DiegoCA92093 United States

Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively utilized for research in brain-machine interfaces. However, the existing devices are often limited in either spatial resolution or cortical coverage, even including those with thousands of channels used in animal experiments. Here, we developed scalable manufacturing processes and dense connectorization to achieve reconfigurable thin-film, multi-thousand channel neurophysiological recording grids using platinum-nanorods (PtNRGrids). With PtNRGrids, we have achieved a multi-thousand channel array of small (30 μm) contacts with low impedance, providing unparalleled spatial and temporal resolution over a large cortical area. We demonstrate that PtNRGrids can resolve sub-millimeter functional organization of the barrel cortex in anesthetized rats that captured the histochemically-demonstrated structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. Additionally, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1 mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrate the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces and highlights a path toward novel therapeutics. Copyright © 2021, The Authors. All rights reserved.

关键词： Mapping

Training of Physical Neural Networks

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

A micromirror array with annular partitioning for high-speed random-access axial focusing

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arXiv 2020年

作者： Ersumo, Nathan Tessema Yalcin, Cem Antipa, Nick Pégard, Nicolas Waller, Laura Lopez, Daniel Muller, Rikky University of California Berkeley - University of California San Francisco Graduate Program in Bioengineering United States Departement of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel HillNC27514 United States Chan Zuckerberg Biohub San FranciscoCA94158 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States

Dynamic axial focusing functionality has recently seen widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics, and material processing. Yet the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to drive (ex: liquid crystal, elastomeric or optofluidic lenses) suffer from low (~100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as dwelling capacity (ex: acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (ex: deformable mirror arrays). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion, 48 μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1 100 nm with 10-90% settling in 64.8 μs (i.e. 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires less than 30 V/channel. Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct, resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor 17

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Design and Implementation of OLED Driving and OPD Readout Ci...

17th IEEE SENSORS Conference, SENSORS 2018

作者： Wu, Yi-Cheng Kao, Yung-Hua Chao, Paul C.-P. Wey, Chin-Long Sauter, Thilo Eka, Fitrah P. Pandey, Rajeev Department of Electrical Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Chiao Tung University Hsinchu City Taiwan EECS International Graduate Program National Chiao Tung University Hsinchu City Taiwan Department for Integrated Sensor Systems Danube University Krems Austria Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781538647073

This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 μm CMOS process, the chip area is 1.012 mm2. The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz1.5 kHz, and resulting in high SNR at frequencies under 60 Hz. © 2018 IEEE.

关键词： Electric power utilization

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

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ACS nano 2025年第23期19卷 2025 Jun 3页

作者： Joseph Cave Anne Christiono Carmine Schiavone Henry J Pownall Vittorio Cristini Daniela I Staquicini Renata Pasqualini Wadih Arap C Jeffrey Brinker Matthew Campen Zhihui Wang Hien Van Nguyen Achraf Noureddine Prashant Dogra Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston Texas 77030 United States. Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York New York 10065 United States. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 United States. Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy. Department of Medicine Houston Methodist Houston Texas 77030 United States. Department of Medicine Weill Cornell Medicine New York New York 10065 United States. Neal Cancer Center Houston Methodist Research Institute Houston Texas 77030 United States. Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston Texas 77030 United States. Rutgers Cancer Institute Newark New Jersey 08901 United States. Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Department of Chemical and Biological Engineering University of New Mexico Albuquerque New Mexico 87106 United States. College of Pharmacy University of New Mexico Albuquerque New Mexico 87106 United States. Department of Physiology and Biophysics Weill Cornell Medical College New York New York 10065 United States. Department of Electrical and Computer Engineering University of Houston Houston Texas 77204 United States.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for settings. To enable organ-specific toxicity predictions , we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

关键词： PBPK artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity

Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

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Coupling Rendering and Generative Adversarial Networks for A...

OCEANS

作者： Albert Reed Isaac D. Gerg John D. McKay Daniel C. Brown David P. Williamsk Suren Jayasuriya School of Electrical Computer & Energy Engineering Arizona State University Tempe AZ USA School of Electrical Engineering and Computer Science Pennsylvania State University State College PA USA Applied Research Laboratory Pennsylvania State University State College PA USA Graduate Program in Acoustics Pennsylvania State University State College PA USA NATO STO Centre for Maritime Research & Experimentation La Spezia Italy School of Arts Media & Engineering Arizona State University Tempe AZ USA

There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.

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PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation

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arXiv 2019年

作者： Jung, Dahuin Bae, Ho Choi, Hyun-Soo Yoon, Sungroh The Dept. of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Dept. of Cyber Security Ewha Womans University Seoul03760 Korea Republic of Dept. of Computer Science & Engineering Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon 24341 Korea Republic of Ziovision Chuncheon24341 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT. Copyright © 2019, The Authors. All rights reserved.

关键词： Steganography

In vivo percutaneous reflectance spectroscopy of fatty liver development in rats suggests that the elevation of the scattering power is an early indicator of hepatic steatosis

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Journal of Innovative Optical Health Sciences 2018年第4期11卷 53-71页

作者： Daqing Piao Jerry W.Ritchey GReed Holyoak Corey R.Wall Nigar Sultana Jill K.Murray Kenneth E.Bartels School of Electrical and Computer Engineering Oklahoma State University 202 Engineering South StillwaterOK 74078USA Department of Veterinary Clinical Sciences Center for Veterinary Health Sciences 002 VTH Oklahoma State University StillwaterOK 74078USA Department of Veterinary Pathobiology Center for Veterinary Health Sciences Oklahoma State University 250 McElroy Hall StillwaterOK 74078USA Graduate Program on Interdisciplinary Sciences Oklahoma State University StillwaterOK 74078USA

This study assessed whether there was a scat tering spectral mar ker quantifiable by reflectance measurements that could indicate early development of hepatic steatosis in rats for potential applications to pre procurement organ *** rats were fed a methionine choline-deficient(MCD)diet and eight rats were fed a normal *** assessment of the liver parenchyma of rats in vivo was performed by percut aneous reflect ance spectroscopy using a single fiber probe at the beginning of diet-intake and arbitrary post-diet-intake times up to 11 weeks to render longitudinal *** sampling of the liver over the duration of diet adm inistration was performed on two MCD diet treated rats and one control rat eutha-nized after reflectance spectroscopy *** images of hematoxylin/eosin-stained liver specimens were analyzed morphometrically to evahuate the lipid size changes associated with the level of *** MCD-diet-treated group(n=16)had mild steatosis in seven rats,moderate in three rats,severe in six rats,and no other significant *** control rats(n=8)developed hepatic *** the parameters retrieved from per-SfS,only the scat tering power(can be either positive or negative)appeared to be statist ically diferent between MCD-treated and control *** scattering power for the 16 MCD-diet-treated livers at the time of euthanasia and presenting various levels of steatosis was 033±0.21,in comparison to 0.036±0.25 of the eight control livers(p=0.0189).When evaluated at days 12 and 13 combined,the scattering power of the 16 MCD-diet-treated livers was 032±0.17,in comparison to 0.10±0.11 of the eight control livers(p=0.0017).All of four MCD-treated livers harvested at days 12 and 13 presented mild steatosis with sub-micron size lipid droplets,even though none of the MCD-treated livers were sonogr aphically remarkable for fatty *** elevation of the scattering power may be a valuable marker indicating ear

关键词： Hepatic steatosis difuse reflect ance spectroscopy liver transplant