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Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections

arXiv

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arXiv 2019年

作者： Pastorelli, Elena Capone, Cristiano Simula, Francesco Sanchez-Vives, Maria V. Del Giudice, Paolo Mattia, Maurizio Paolucci, Pier Stanislao INFN Sezione di Roma Rome Italy PhD Program in Behavioural Neuroscience Sapienza University Rome Italy Istituto Superiore di Sanità Rome Italy Systems Neuroscience IDIBAPS Barcelona Spain ICREA Barcelona Spain

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192×192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying intermodular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rate. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3×109and 4.1×109synaptic events per second for both cortical states in the explored range of inter-modular interconnections. Copyright © 2019, The Authors. All rights reserved.

关键词： Message passing

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Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques

arXiv

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arXiv 2018年

作者： Celotto, Marco de Luca, Chiara Muratore, Paolo Resta, Francesco Mascaro, Anna Letizia Allegra Pavone, Francesco Saverio de Bonis, Giulia Paolucci, Pier Stanislao Department of Physics "Sapienza" University of Rome Rome00185 Italy LENS University of Florence Florence50019 Italy Istituto di Neuroscienze CNR Pisa56124 Italy Department of Physics University of Florence Florence50019 Italy INFN Rome00185 Italy IIT - Neural Computation Lab CNCS@UniTn Rovereto38068 Italy PhD Program in Behavioural Neuroscience "Sapienza" University of Rome Rome00185 Italy PhD Program in Cognitive Neuroscience SISSA Trieste34136 Italy

Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA). Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping and awake spiking model

arXiv

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arXiv 2022年

作者： De Luca, Chiara Tonielli, Leonardo Pastorelli, Elena Capone, Cristiano Simula, Francesco Lupo, Cosimo Bernava, Irene Tiddia, Gianmarco De Bonis, Giulia Golosio, Bruno Paolucci, Pier Stanislao Istituto Nazionale di Fisica Nucleare Sezione di Roma Italy PhD Program in Behavioural Neuroscience "Sapienza" University of Rome Italy Dipartimento di Fisica Università di Cagliari Italy Istituto Nazionale di Fisica Nucleare Sezione di Cagliari Italy

Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes learning, supports creativity, and manages the energy consumption of networks engaged in post-sleep task have not been yet modelled. During sleep, the brain cycles between non-rapid eye movement (NREM), a mainly unconscious state characterized by collective oscillations, and rapid eye movement (REM), associated with the integrated experience of dreaming. We propose a biologically grounded two-area thalamo-cortical plastic spiking neural network model and investigate the role of NREM - REM cycles on its awake performance. We demonstrate that sleep has a positive effect on energy consumption and cognitive performance during the post-sleep awake classification task of handwritten digits. NREM and REM simulated dynamics modify the synaptic structure into a sharper representation of training experiences. Sleep-induced synaptic modifications reduce firing rates and synaptic activity without reducing cognitive performance. Also, it creates novel multi-area associations. The model leverages the apical amplification, isolation and drive experimentally grounded principles and the combination of contextual and perceptual information. In summary, the main novelty is the proposal of a multi-area plastic model that also expresses REM and integrates information during a plastic dream-like state, with cognitive and energetic benefits during post-sleep awake classification. © 2022, CC BY.

关键词： Neural network models

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Real-time cortical simulations: Energy and interconnect scaling on distributed systems

arXiv

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arXiv 2018年

作者： Simula, Francesco Pastorelli, Elena Paolucci, Pier Stanislao Martinelli, Michele Lonardo, Alessandro Biagioni, Andrea Capone, Cristiano Capuani, Fabrizio Cretaro, Paolo de Bonis, Giulia Cicero, Francesca Lo Pontisso, Luca Vicini, Piero Ammendola, Roberto INFN Sezione di Roma Rome Italy INFN Sezione di Roma and PhD Program in Behavioural Neuroscience "Sapienza" University of Rome Rome Italy INFN Sezione di Tor Vergata and Electronic Engineering Dept. University of Roma "Tor Vergata" Rome Italy

We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of processor architectures typical of standard HPC and embedded platforms are compared. We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption. The cost of cortical simulations is quantified using the Joule per synaptic event metric on both architectures. Reaching efficient real-time on large scale cortical simulations is of increasing relevance for both future bio-inspired artificial intelligence applications and for understanding the cognitive functions of the brain, a scientific quest that will require to embed large scale simulations into highly complex virtual or real worlds. This work stands at the crossroads between the WaveScalES experiment in the Human Brain Project (HBP), which includes the objective of large scale thalamo-cortical simulations of brain states and their transitions, and the ExaNeSt and EuroExa projects, that investigate the design of an ARM-based, low-power High Performance Computing (HPC) architecture with a dedicated interconnect scalable to million of cores;simulation of deep sleep Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by thalamo-cortical models are among their benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Energy utilization

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Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

arXiv

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arXiv 2018年

作者： Pastorelli, Elena Paolucci, Pier Stanislao Simula, Francesco Biagioni, Andrea Capuani, Fabrizio Cretaro, Paolo De Bonis, Giulia Lo Cicero, Francesca Lonardo, Alessandro Martinelli, Michele Pontisso, Luca Vicini, Piero Ammendola, Roberto INFN Sezione di Roma PhD Program in Behavioural Neuroscience Sapienza University of Rome Rome Italy INFN Sezione di Roma Rome Italy INFN Sezione di Tor Vergata Electronic Engineering Dept. University of Roma Tor Vergata Rome Italy

We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays. While previous studies adopted short-range connectivity, recent experimental neurosciences studies are pointing out the role of longer-range intra-areal connectivity with implications on neural simulation platforms. Two-dimensional grids of cortical columns composed by up to 11 M point-like spiking neurons with spike frequency adaption were connected by up to 30 G synapses using short- and long-range connectivity models. The MPI processes composing the distributed simulator were run on up to 1024 hardware cores, hosted on a 64 nodes server platform. The hardware platform was a cluster of IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell 8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through an InfiniBand network, equipped with 4× QDR switches. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

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Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

arXiv

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arXiv 2021年

作者： Capone, Cristiano De Luca, Chiara De Bonis, Giulia Gutzen, Robin Bernava, Irene Pastorelli, Elena Simula, Francesco Lupo, Cosimo Tonielli, Leonardo Resta, Francesco Mascaro, Anna Letizia Allegra Pavone, Francesco Denker, Michael Paolucci, Pier Stanislao INFN Sezione di Roma Italy European Laboratory for Non-Linear Spectroscopy Sesto Fiorentino Italy Neuroscience Institute National Research Council Pisa Italy University of Florence Physics and Astronomy Department Sesto Fiorentino Italy Jülich Research Centre Jülich Germany Theoretical Systems Neurobiology RWTH Aachen University Aachen Germany PhD Program in Behavioural Neuroscience "Sapienza" University of Rome Italy

The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists. Copyright © 2021, The Authors. All rights reserved.

关键词： Brain

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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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Nature medicine 2024年第12期30卷 3779页

作者： Sebastian Moguilner Sandra Baez Hernan Hernandez Joaquín Migeot Agustina Legaz Raul Gonzalez-Gomez Francesca R Farina Pavel Prado Jhosmary Cuadros Enzo Tagliazucchi Florencia Altschuler Marcelo Adrián Maito María E Godoy Josephine Cruzat Pedro A Valdes-Sosa Francisco Lopera John Fredy Ochoa-Gómez Alfredis Gonzalez Hernandez Jasmin Bonilla-Santos Rodrigo A Gonzalez-Montealegre Renato Anghinah Luís E d'Almeida Manfrinati Sol Fittipaldi Vicente Medel Daniela Olivares Görsev G Yener Javier Escudero Claudio Babiloni Robert Whelan Bahar Güntekin Harun Yırıkoğulları Hernando Santamaria-Garcia Alberto Fernández Lucas David Huepe Gaetano Di Caterina Marcio Soto-Añari Agustina Birba Agustin Sainz-Ballesteros Carlos Coronel-Oliveros Amanuel Yigezu Eduar Herrera Daniel Abasolo Kerry Kilborn Nicolás Rubido Ruaridh A Clark Ruben Herzog Deniz Yerlikaya Kun Hu Mario A Parra Pablo Reyes Adolfo M García Diana L Matallana José Alberto Avila-Funes Andrea Slachevsky María I Behrens Nilton Custodio Juan F Cardona Pablo Barttfeld Ignacio L Brusco Martín A Bruno Ana L Sosa Ortiz Stefanie D Pina-Escudero Leonel T Takada Elisa Resende Katherine L Possin Maira Okada de Oliveira Alejandro Lopez-Valdes Brian Lawlor Ian H Robertson Kenneth S Kosik Claudia Duran-Aniotz Victor Valcour Jennifer S Yokoyama Bruce Miller Agustin Ibanez Latin American Brain Health Institute Universidad Adolfo Ibañez Santiago de Chile Chile. Cognitive Neuroscience Center Universidad de San Andrés Buenos Aires Argentina. Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA. Universidad de los Andes Bogota Colombia. Global Brain Health Institute (GBHI) University of California San Francisco CA USA. Global Brain Health Institute (GBHI) Trinity College Dublin Dublin Ireland. The University of California Santa Barbara (UCSB) Santa Barbara CA USA. Escuela de Fonoaudiología Universidad San Sebastián Santiago de Chile Chile. Grupo de Bioingeniería Decanato de Investigación Universidad Nacional Experimental del Táchira San Cristóbal Venezuela. Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile. University of Buenos Aires Buenos Aires Argentina. The Clinical Hospital of Chengdu Brain Sciences Institute University of Electronic Sciences and Technology of China Chengdu China. Technology of China Chengdu China. Cuban Neuroscience Center La Habana Cuba. Grupo de Neurociencias de Antioquia (GNA) University of Antioquia Medellín Colombia. Department of Psychology Master Program of Clinical Neuropsychology Universidad Surcolombiana Neiva Neiva Colombia. Department of Psychology Universidad Cooperativa de Colombia Arauca Colombia. Neurocognition and Psychophysiology Laboratory Universidad Surcolombiana Neiva Colombia. Reference Center of Behavioural Disturbances and Dementia School of Medicine University of Sao Paulo Sao Paulo Brazil. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center University of Sao Paulo Sao Paulo Brazil. Center for Social and Cognitive Neuroscience School of Psychology Universidad Adolfo Ibáñez Santiago Chile. Neuropsychology and Clinical Neuroscience Laboratory (LANNEC) Physiopathology Program-Institute of Biomedical Sciences (ICBM) Neuroscience and East Neuroscience Dep

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Proceedings of the 14th annual conference of INEBRIA

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Addiction Science & Clinical Practice 2017年第1期12卷 1-24页

作者： Aisha S. Holloway Sarah Landale Laura Cariola Jennifer Ferguson Dorothy Newbury-Birch Amy Flynn John R. Knight Lon Sherritt Sion K. Harris Amy J. O’Donnell Eileen Kaner Barbara Hanratty Denise Howel Elaine McColl Ruth McGovern Stephanie Scott Elaine Stamp Ellen Lynch Amy M. Loree Steve Martino Amy C. Justice Kimberly A. Yonkers Kate Gilstead-Hayden Steven J. Ondersma Joy Scheidell Angeline Adam Jennifer McNeely Robert P. Schwartz Li-Tzy Wu Geetha Subramaniam Gaurav Sharma Anne H. Berman Karoline Kolaas Elisabeth Petersén Erik Hedman Kristina Sinadinovic Anna Thurang Sven Andréasson Ingvar Rosendahl Mikael Gajecki Preben Bendtsen Catharina Linderoth Ulrika Müssener Fredrik Spak Ida Gremyr Ann M. Mitchell Brayden N. Kameg Deborah Finnell Christine L. Savage Khadejah F. Mahmoud Benjamin C. Riordan Tamlin S. Conner Jayde A. M. Flett Damian Scarf Bonnie McRee Janice Vendetti Karen Steinberg Gallucci Kate Robaina Brendan J. Clark Kathryne D. Reed Rachel M. Hodapp Ivor Douglas Ellen L. Burnham Jacqueline Jones Laura Aagaard Paul F. Cook Brett R. Harris Jiang Yu Margaret Wolff Meighan Rogers Carolina Barbosa Brendan J. Wedehase Laura J. Dunlap Shannon G. Mitchell Kristi A. Dusek Jan Gryczynski Arethusa S. Kirk Marla T. Oros Colleen Hosler Kevin E. O’Grady Barry S. Brown Colin Angus Sidney Sherborne Duncan Gillespie Petra Meier Alan Brennan Divane de Vargas Janaina Soares Donna Castelblanco Ian Wittman Ryan P. McCormack Mirelis Gonzalez Kelly M. Doran Donna Shelley John Rotrosen Lillian Gelberg E. Jennifer Edelman Christopher J. Cutter Lynn E. Fiellin Patrick G. O’Connor David A. Fiellin Nathan B. Hansen Stephen A. Maisto Yanhong Deng James Dziura Roger Bedimo Cynthia Gibert Vincent C. Marconi David Rimland Maria C. Rodriguez-Barradas Michael S. Simberkoff Kendall J. Bryant Emma L. Giles Grant J. McGeechan Simon Coulton Nadine Hendrie Paolo Deluca Colin Drummond Sadie Boniface Amy Ramsey Harry Sumnall Luke Vale Viviana Alabani Amanda Atkinson Grant Stanley Jo Frankham Eilish Gilvarry Nicola Howe Justine Clephane David Gar Nursing Studies School of Health in Social Science The University of Edinburgh Edinburgh UK Health and Social Care Institute School of Health and Social Care Teesside University Middlesbrough Tees Valley UK School of Health and Social Care Teesside University Middlesbrough Tees Valley UK Alcohol and Public Health Team School of Health and Social Care Middlesbrough Teesside UK Department of Pediatrics Harvard Medical School Boston MA USA Center for Adolescent Substance Abuse Research Boston Children’s Hospital Boston MA USA Institute of Health and Society Newcastle University Baddiley-Clark Building Richardson Road Newcastle Upon Tyne Northumberland UK VA Connecticut Healthcare System Veterans Aging Cohort Study West Haven CT USA Department of Psychiatry Yale University School of Medicine New Haven CT USA Department of Psychiatry Yale School of Medicine VA Connecticut Healthcare New Haven CT USA Department of Psychiatry Yale School of Medicine VA Connecticut Healthcare System West Haven CT USA Yale University School of Medicine New Haven CT USA Merrill-Palmer Skillman Institute and Department of Psychiatry and Behavioral Neurosciences Wayne State University Detroit MI USA Department of Psychiatry and Behavioral Neurosciences Merrill-Palmer Skillman Institute Detroit MI USA Department of Psychiatry and Behavioral Neurosciences and Merrill-Palmer Skillman Institute Wayne State University Detroit MI USA Department of Population Health NYU School of Medicine New York NY USA Friends Research Institute Inc Baltimore MD USA Department of Psychiatry and Behavioral Sciences Department of Medicine and Duke Clinical Research Institute Duke University Medical Center Durham NC USA Center for the Clinical Trials Network National Institute on Drug Abuse National Institutes of Health Bethesda MD USA The EMMES Corporation Rockville MD USA Department of Clinical Neuroscience Center for Psychiatry Research Karolinska Institutet Stockholm Sweden

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