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Concept transfer of synaptic diversity from biological to artificial neural networks

Nature communications 2025年第1期16卷 5112页

作者： Martin Hofmann Moritz Franz Peter Becker Christian Tetzlaff Patrick Mäder Data-intensive Systems and Visualization Group (dAI.SY) Technische Universität Ilmenau Max-Planck-Ring 14 Ilmenau 98693 Thuringia Germany. martin.hofmann@tu-ilmenau.de. Group of Computational Synaptic Physiology Department for Neuro- and Sensory Physiology University Medical Center Göttingen Humboldtallee 23 Göttingen 37073 Lower Saxony Germany. Campus-Institut Data Science (CIDAS) University of Göttingen Goldschmidtstraße 1 Göttingen 37077 Lower Saxony Germany. Data-intensive Systems and Visualization Group (dAI.SY) Technische Universität Ilmenau Max-Planck-Ring 14 Ilmenau 98693 Thuringia Germany. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Deutscher Platz 5e Leipzig 04103 Saxony Germany. Faculty of Biological Sciences Friedrich Schiller University Fürstengraben 1 Jena 07745 Thuringia Germany.

Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provided new insights into the mechanisms of biological neural networks, only a limited number of these concepts have been directly applied to artificial neural networks, with no guarantee of improved performance. Here, we address the discrepancy between the inhomogeneous and dynamic structures of biological neural networks and the largely homogeneous and fixed topologies of artificial neural networks. Specifically, we demonstrate successful integration of concepts of synaptic diversity, including spontaneous spine remodeling, synaptic plasticity diversity, and multi-synaptic connectivity, into artificial neural networks. Our findings reveal increased learning speed, prediction accuracy, and resilience to gradient inversion attacks. Our publicly available drop-in replacement code enables easy incorporation of these proposed concepts into existing networks.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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USID and Pycroscopy - Open frameworks for storing and analyzing spectroscopic and imaging data

arXiv

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arXiv 2019年

作者： Somnath, Suhas Smith, Chris R. Laanait, Nouamane Vasudevan, Rama K. Ievlev, Anton Belianinov, Alex Lupini, Andrew R. Shankar, Mallikarjun Kalinin, Sergei V. Jesse, Stephen Advanced Data and Workflows Group National Center for Computational Sciences Center for Nanophase Materials Sciences Computational Chemical and Materials Sciences Computational Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud-computing. Here, we describe Universal Spectroscopy and Imaging data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of open-science. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data. Copyright © 2019, The Authors. All rights reserved.

关键词： Open systems

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Relativistic slingshot: A source for single circularly polarized attosecond x-ray pulses

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Physical Review E 2020年第6期102卷 061201(R)-061201(R)页

作者： Jingwei Wang Sergei V. Bulanov Min Chen Bifeng Lei Yuxue Zhang Rishat Zagidullin Veronika Zorina Wei Yu Yuxin Leng Ruxin Li Matt Zepf Sergey G. Rykovanov State Key Laboratory of High Field Laser Physics CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine Mechanics (SIOM) Chinese Academy of Sciences (CAS) Shanghai 201800 China Collaborative Innovation Center of IFSA Shanghai Jiao Tong University Shanghai 200240 China Institute of Physics ASCR v.v.i. (FZU) ELI-Beamlines Project 182 21 Prague Czech Republic Key Laboratory for Laser Plasmas (MoE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Helmholtz Institute Jena Fröbelstieg 3 07743 Jena Germany Faculty of Physics and Astronomy Friedrich-Schiller-Universität Jena 07743 Jena Germany Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow 121205 Russia

We propose a mechanism to generate a single intense circularly polarized attosecond x-ray pulse from the interaction of a circularly polarized relativistic few-cycle laser pulse with an ultrathin foil at normal incidence. Analytical modeling and particle-in-cell simulation demonstrate that a huge charge-separation field can be produced when all the electrons are displaced from the target by the incident laser, resulting in a high-quality relativistic electron mirror that propagates against the tail of the laser pulse. The latter is efficiently reflected as well as compressed into an attosecond pulse that is also circularly polarized.

关键词： High intensity laser-plasma interactions X-ray generation in plasmas Relativistic plasmas Particle-in-cell methods Test-particle methods

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Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking

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International Journal of Radiation Oncology Biology Physics 2025年

作者： Song, Yuze Dornisch, Anna M. Dess, Robert T. Margolis, Daniel J.A. Weinberg, Eric P. Barrett, Tristan Cornell, Mariel Fan, Richard E. Harisinghani, Mukesh Kamran, Sophia C. Lee, Jeong Hoon Li, Cynthia Xinran Liss, Michael A. Rusu, Mirabela Santos, Jason Sonn, Geoffrey A. Vidic, Igor Woolen, Sean A. Dale, Anders M. Seibert, Tyler M. Department of Radiation Medicine and Applied Sciences University of California San Diego La JollaCalifornia United States Department of Electrical and Computer Engineering University of California San Diego La JollaCalifornia United States Department of Radiation Oncology University of Michigan Ann ArborMI United States Department of Radiology Cornell University IthacaNew York United States Department of Clinical Imaging Sciences University of Rochester Medical Center RochesterNew York United States Department of Radiology University of Cambridge United Kingdom Cambridge United Kingdom Radformation New YorkNew York United States Department of Urology Stanford School of Medicine Palo AltoCalifornia United States Department of Radiology Massachusetts General Hospital BostonMassachusetts United States Department of Radiation Oncology Massachusetts General Hospital BostonMassachusetts United States Department of Radiology Stanford School of Medicine Palo AltoCalifornia United States Institute for Computational and Mathematical Engineering Stanford University Palo AltoCalifornia United States Department of Urology University of Texas Health Sciences Center San Antonio San AntonioTexas United States Department of Biomedical Data Science Stanford University Palo AltoCalifornia United States Quibim New YorkNew York United States Cortechs.ai San DiegoCalifornia United States Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCalifornia United States Department of Radiology University of California San Diego La JollaCalifornia United States Department of Neurosciences University of California San Diego La JollaCalifornia United States Halıcıoğlu Data Science Institute University of California San Diego La JollaCalifornia United States Department of Bioengineering University of California San Diego La JollaCalifornia United States Department of Urology University of California San Diego La Jolla

Purpose: Evaluation of artificial intelligence (AI) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to: (1) create a reference standard data set with precise prostate contours by expert consensus, and (2) evaluate various AI tools against this standard. Methods and Materials: We obtained prostate magnetic resonance imaging cases from six institutions from the Qualitative Prostate Imaging Consortium. A panel of 4 experts (2 genitourinary radiologists and 2 prostate radiation oncologists) meticulously developed consensus prostate segmentations on axial T2-weighted series. We evaluated the performance of 6 AI tools (3 commercially available and 3 academic) using Dice scores, distance from reference contour, and volume error. Results: The panel achieved consensus prostate segmentation on each slice of all 68 patient cases included in the reference data set. We present 2 patient examples to serve as contouring guides. Depending on the AI tool, median Dice scores (across patients) ranged from 0.80 to 0.94 for whole prostate segmentation. For a typical (median) patient, AI tools had a mean error over the prostate surface ranging from 1.3 to 2.4 mm. They maximally deviated 3.0 to 9.4 mm outside the prostate and 3.0 to 8.5 mm inside the prostate for a typical patient. Error in prostate volume measurement for a typical patient ranged from 4.3% to 31.4%. Conclusions: We established an expert consensus benchmark for prostate segmentation. The best-performing AI tools have typical accuracy greater than that reported for radiation oncologists using computed tomography scans (the most common clinical approach for radiation therapy planning). Physician review remains essential to detect occasional major errors. © 2025 The Authors

关键词： Volume measurement

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Corrigendum to “GLRM: Logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy” [Int. J. Approx. Reason. 143 (2022) 78–101]

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International Journal of Approximate Reasoning 2022年 151卷 271-271页

作者： Qian Guo Yuhua Qian Xinyan Liang Institute of Big Data Science and Industry Shanxi University Taiyuan 030006 Shanxi China Engineering Research Center for Machine Vision and Data Mining of Shanxi Province Taiyuan 030006 Shanxi China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan 030006 Shanxi China

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Evaluating deep transfer learning for whole-brain cognitive decoding

arXiv

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arXiv 2021年

作者： Thomas, Armin W. Lindenberger, Ulman Samek, Wojciech Müller, Klaus-Robert Machine Learning Group Dept. of Computer Science and Electrical Engineering Technische Universität Berlin Berlin Germany Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Stanford Data Science Stanford University StanfordCA United States Dept. of Psychology Stanford University StanfordCA United States Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Dept. of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. © 2021, CC BY.

关键词： Decoding

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Synchronization of active rotators interacting with environment

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Physical Review E 2020年第2期101卷 022613-022613页

作者： Taegeun Song Heetae Kim Seung-Woo Son Junghyo Jo Department of Physics Pohang University of Science and Technology (POSTECH) Pohang 37673 Korea Data Science Institute Faculty of Engineering Universidad del Desarrollo Santiago 7610658 Chile Asia Pacific Center for Theoretical Physics (APCTP) Pohang 37673 Korea Department of Applied Physics Hanyang University Ansan 15588 Korea Department of Statistics Keimyung University Daegu 42601 Korea School of Computational Sciences Korea Institute for Advanced Study Seoul 02455 Korea Department of Physics Education and Center for Theoretical Physics Seoul National University Seoul 08826 Korea

Multiple organs in a living system respond to environmental changes, and the signals from the organs regulate the physiological environment. Inspired by this biological feedback, we propose a simple autonomous system of active rotators to explain how multiple units are synchronized under a fluctuating environment. We find that the feedback via an environment can entrain rotators to have synchronous phases for specific conditions. This mechanism is markedly different from the simple entrainment by a common oscillatory external stimulus that is not interacting with systems. We theoretically examine how the phase synchronization depends on the interaction strength between rotators and environment. Furthermore, we successfully demonstrate the proposed model by realizing an analog electric circuit with microelectronic devices. This bioinspired platform can be used as a sensor for monitoring varying environments and as a controller for amplifying signals by their feedback-induced synchronization.

关键词： Synchronization Hormones

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Evolutionary Structural Optimization Algorithm Based on FFT-JVIE Solver for Inverse Design of Wave Devices

Evolutionary Structural Optimization Algorithm Based on FFT-...

引用

engineering and Telecommunication (EnT-MIPT)

作者： Iurii B. Minin Egor E. Nuzhin Alexey I. Boyko Mikhail S. Litsarev Ivan V. Oseledets Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (纸本)9781728104331

We propose a topology optimization method with a target function based on FFT-JVIE solver. It allows to find an optimal structure in a 3D space for homogeneous objects (lenses and metallic reflectors). Such examples as glass lenses, parabolic reflectors are considered. Numerical experiments show that discrete gradient algorithm converges much faster than greedy algorithm for all considered cases.

关键词： Optimization Two dimensional displays Three-dimensional displays Topology Tensors Lenses

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Correction to: Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

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intensive care medicine 2022年第8期48卷 1130-1131页

作者： Massimiliano Greco Thomas De Corte Ari Ercole Massimo Antonelli Elie Azoulay Giuseppe Citerio Andy Conway Morris Gennaro De Pascale Frantisek Duska Paul Elbers Sharon Einav Lui Forni Laura Galarza Armand R J Girbes Giacomo Grasselli Vitaly Gusarov Alasdair Jubb Jozef Kesecioglu Andrea Lavinio Maria Cruz Martin Delgado Johannes Mellinghoff Sheila Nainan Myatra Marlies Ostermann Mariangela Pellegrini Pedro Povoa Stefan J Schaller Jean-Louis Teboul Adrian Wong Jan J De Waele Maurizio Cecconi Department of Biomedical Sciences Humanitas University Via Rita Levi Montalcini 4 Pieve Emanuele 20072 Milan Italy. IRCCS Humanitas Research Hospital Via Manzoni 56 Rozzano 20089 Milan Italy. Department of Internal Medicine and Pediatrics Faculty of Medicine and Health Sciences Ghent University Ghent Belgium. Department of Intensive Care Medicine Ghent University Hospital Ghent Belgium. Cambridge Centre for Artificial Intelligence in Medicine University of Cambridge Cambridge UK. University of Cambridge Division of Anaesthesia Addenbrooke's Hospital Hills Road Cambridge UK. Dipartimento di Scienze dell'Emergenza Anestesiologiche e della Rianimazione Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore Rome Italy. Istituto di Anestesiologia e Rianimazione Università Cattolica del Sacro Cuore Rome Italy. Médecine Intensive et Réanimation APHP Saint-Louis Hospital Paris University Paris France. Université de Paris Paris France. School of Medicine and Surgery University of Milano-Bicocca Milan Italy. Department Neuroscience Neurointensive Care ASST-Monza Monza Italy. Division of Anaesthesia Department of Medicine University of Cambridge Cambridge UK. Division of Immunology Department of Pathology University of Cambridge Cambridge UK. JVF Intensive Care Unit Addenbrookes Hospital Cambridge UK. Department of Anaesthesia and Intensive Care Third Faculty of Medicine Charles University Prague Czech Republic. FNKV University Hospital in Prague Prague Czech Republic. Department of Intensive Care Medicine Laboratory of Critical Care Computational Intelligence Amsterdam UMC Vrije Universiteit Amsterdam The Netherlands. General Intensive Care Unit of the Shaare Zedek Medical Center Jerusalem Israel. Faculty of Medicine Hebrew University Jerusalem Israel. Department of Critical Care Royal Surrey Hospital and Faculty of Experimental Medicine University of Surrey Guildford UK. Intensive Care Unit Hospital General Un

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