1
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Savtchenko LP, Rusakov DA. Equal levels of pre- and postsynaptic potentiation produce unequal outcomes. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230235. [PMID: 38853561 PMCID: PMC11343314 DOI: 10.1098/rstb.2023.0235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/06/2023] [Accepted: 01/05/2024] [Indexed: 06/11/2024] Open
Abstract
Which proportion of the long-term potentiation (LTP) expressed in the bulk of excitatory synapses is postsynaptic and which presynaptic remains debatable. To understand better the possible impact of either LTP form, we explored a realistic model of a CA1 pyramidal cell equipped with known membrane mechanisms and multiple, stochastic excitatory axo-spinous synapses. Our simulations were designed to establish an input-output transfer function, the dependence between the frequency of presynaptic action potentials triggering probabilistic synaptic discharges and the average frequency of postsynaptic spiking. We found that, within the typical physiological range, potentiation of the postsynaptic current results in a greater overall output than an equivalent increase in presynaptic release probability. This difference grows stronger at lower input frequencies and lower release probabilities. Simulations with a non-hierarchical circular network of principal neurons indicated that equal increases in either synaptic fidelity or synaptic strength of individual connections also produce distinct changes in network activity, although the network phenomenology is likely to be complex. These observations should help to interpret the machinery of LTP phenomena documented in situ. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.
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Affiliation(s)
- Leonid P. Savtchenko
- UCL Queen Square Institute of Neurology, University College London, LondonWC1N 3BG, UK
| | - Dmitri A. Rusakov
- UCL Queen Square Institute of Neurology, University College London, LondonWC1N 3BG, UK
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2
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Linne ML. Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning. Curr Opin Neurobiol 2024; 85:102838. [PMID: 38310660 DOI: 10.1016/j.conb.2023.102838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 02/06/2024]
Abstract
Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used. Based on the accumulated data, computational models of the neuron-astrocyte interactions have been developed to explain the complex functions of astrocytes in neural circuits and to test conflicting hypotheses. This review presents the most significant recent models, modeling methods and simulation tools for neuron-astrocyte interactions. In the future, we will especially need more experimental research on awake animals in vivo and new computational models of neuron-glia interactions to advance our understanding of cellular dynamics and the functioning of neural circuits in different brain regions.
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Affiliation(s)
- Marja-Leena Linne
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
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3
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Brazhe A, Verisokin A, Verveyko D, Postnov D. Astrocytes: new evidence, new models, new roles. Biophys Rev 2023; 15:1303-1333. [PMID: 37975000 PMCID: PMC10643736 DOI: 10.1007/s12551-023-01145-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 09/08/2023] [Indexed: 11/19/2023] Open
Abstract
Astrocytes have been in the limelight of active research for about 3 decades now. Over this period, ideas about their function and role in the nervous system have evolved from simple assistance in energy supply and homeostasis maintenance to a complex informational and metabolic hub that integrates data on local neuronal activity, sensory and arousal context, and orchestrates many crucial processes in the brain. Rapid progress in experimental techniques and data analysis produces a growing body of data, which can be used as a foundation for formulation of new hypotheses, building new refined mathematical models, and ultimately should lead to a new level of understanding of the contribution of astrocytes to the cognitive tasks performed by the brain. Here, we highlight recent progress in astrocyte research, which we believe expands our understanding of how low-level signaling at a cellular level builds up to processes at the level of the whole brain and animal behavior. We start our review with revisiting data on the role of noradrenaline-mediated astrocytic signaling in locomotion, arousal, sensory integration, memory, and sleep. We then briefly review astrocyte contribution to the regulation of cerebral blood flow regulation, which is followed by a discussion of biophysical mechanisms underlying astrocyte effects on different brain processes. The experimental section is closed by an overview of recent experimental techniques available for modulation and visualization of astrocyte dynamics. We then evaluate how the new data can be potentially incorporated into the new mathematical models or where and how it already has been done. Finally, we discuss an interesting prospect that astrocytes may be key players in important processes such as the switching between sleep and wakefulness and the removal of toxic metabolites from the brain milieu.
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Affiliation(s)
- Alexey Brazhe
- Department of Biophysics, Biological Faculty, Lomonosov Moscow State University, Leninskie Gory, 1/24, Moscow, 119234 Russia
- Department of Molecular Neurobiology, Institute of Bioorganic Chemistry RAS, GSP-7, Miklukho-Maklay Str., 16/10, Moscow, 117997 Russia
| | - Andrey Verisokin
- Department of Theoretical Physics, Kursk State University, Radishcheva st., 33, Kursk, 305000 Russia
| | - Darya Verveyko
- Department of Theoretical Physics, Kursk State University, Radishcheva st., 33, Kursk, 305000 Russia
| | - Dmitry Postnov
- Department of Optics and Biophotonics, Saratov State University, Astrakhanskaya st., 83, Saratov, 410012 Russia
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4
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Magloire V, Savtchenko LP, Jensen TP, Sylantyev S, Kopach O, Cole N, Tyurikova O, Kullmann DM, Walker MC, Marvin JS, Looger LL, Hasseman JP, Kolb I, Pavlov I, Rusakov DA. Volume-transmitted GABA waves pace epileptiform rhythms in the hippocampal network. Curr Biol 2023; 33:1249-1264.e7. [PMID: 36921605 PMCID: PMC10615848 DOI: 10.1016/j.cub.2023.02.051] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 01/05/2023] [Accepted: 02/15/2023] [Indexed: 03/17/2023]
Abstract
Mechanisms that entrain and pace rhythmic epileptiform discharges remain debated. Traditionally, the quest to understand them has focused on interneuronal networks driven by synaptic GABAergic connections. However, synchronized interneuronal discharges could also trigger the transient elevations of extracellular GABA across the tissue volume, thus raising tonic conductance (Gtonic) of synaptic and extrasynaptic GABA receptors in multiple cells. Here, we monitor extracellular GABA in hippocampal slices using patch-clamp GABA "sniffer" and a novel optical GABA sensor, showing that periodic epileptiform discharges are preceded by transient, region-wide waves of extracellular GABA. Neural network simulations that incorporate volume-transmitted GABA signals point to a cycle of GABA-driven network inhibition and disinhibition underpinning this relationship. We test and validate this hypothesis using simultaneous patch-clamp recordings from multiple neurons and selective optogenetic stimulation of fast-spiking interneurons. Critically, reducing GABA uptake in order to decelerate extracellular GABA fluctuations-without affecting synaptic GABAergic transmission or resting GABA levels-slows down rhythmic activity. Our findings thus unveil a key role of extrasynaptic, volume-transmitted GABA in pacing regenerative rhythmic activity in brain networks.
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Affiliation(s)
- Vincent Magloire
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
| | - Leonid P Savtchenko
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
| | - Thomas P Jensen
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Sergyi Sylantyev
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK; Rowett Institute, University of Aberdeen, Ashgrove Road West, Aberdeen AB25 2ZD, UK
| | - Olga Kopach
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Nicholas Cole
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Olga Tyurikova
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Dimitri M Kullmann
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Jonathan S Marvin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Loren L Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA; Howard Hughes Medical Institute, University of California, San Diego, La Jolla, CA 92093, USA; GENIE Project Team, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jeremy P Hasseman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA; GENIE Project Team, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ilya Kolb
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA; GENIE Project Team, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ivan Pavlov
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Dmitri A Rusakov
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
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5
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Manninen T, Aćimović J, Linne ML. Analysis of Network Models with Neuron-Astrocyte Interactions. Neuroinformatics 2023; 21:375-406. [PMID: 36959372 PMCID: PMC10085960 DOI: 10.1007/s12021-023-09622-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 03/25/2023]
Abstract
Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes' roles in brain functions.
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Affiliation(s)
- Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland.
| | - Jugoslava Aćimović
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland.
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6
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Cobb EAW, Petroccione MA, Scimemi A. NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON. Sci Rep 2023; 13:464. [PMID: 36627356 PMCID: PMC9832141 DOI: 10.1038/s41598-022-27302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.
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Affiliation(s)
- Evan A. W. Cobb
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA ,grid.265850.c0000 0001 2151 7947Department of Computer Science, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Maurice A. Petroccione
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Annalisa Scimemi
- Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY, 12222-0100, USA.
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7
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Linne ML, Aćimović J, Saudargiene A, Manninen T. Neuron-Glia Interactions and Brain Circuits. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:87-103. [PMID: 35471536 DOI: 10.1007/978-3-030-89439-9_4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital role in supporting and expanding the functions of brain circuits and networks. Despite this accumulated knowledge, the field of computational neuroscience has mostly focused on modeling neuronal functions, ignoring the glial cells and the interactions they have with the neurons. In this chapter, we introduce the biology of neuron-glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain functions in the future.
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Affiliation(s)
- Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Jugoslava Aćimović
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Ausra Saudargiene
- Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania.,Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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8
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Lazar AA, Liu T, Turkcan MK, Zhou Y. Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era. eLife 2021; 10:e62362. [PMID: 33616035 PMCID: PMC8016480 DOI: 10.7554/elife.62362] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 02/21/2021] [Indexed: 11/25/2022] Open
Abstract
In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.
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Affiliation(s)
- Aurel A Lazar
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
| | - Tingkai Liu
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
| | | | - Yiyin Zhou
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
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9
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Lenk K, Satuvuori E, Lallouette J, Ladrón-de-Guevara A, Berry H, Hyttinen JAK. A Computational Model of Interactions Between Neuronal and Astrocytic Networks: The Role of Astrocytes in the Stability of the Neuronal Firing Rate. Front Comput Neurosci 2020; 13:92. [PMID: 32038210 PMCID: PMC6987305 DOI: 10.3389/fncom.2019.00092] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 12/20/2019] [Indexed: 12/12/2022] Open
Abstract
Recent research in neuroscience indicates the importance of tripartite synapses and gliotransmission mediated by astrocytes in neuronal system modulation. Although the astrocyte and neuronal network functions are interrelated, they are fundamentally different in their signaling patterns and, possibly, the time scales at which they operate. However, the exact nature of gliotransmission and the effect of the tripartite synapse function at the network level are currently elusive. In this paper, we propose a computational model of interactions between an astrocyte network and a neuron network, starting from tripartite synapses and spanning to a joint network level. Our model focuses on a two-dimensional setup emulating a mixed in vitro neuron-astrocyte cell culture. The model depicts astrocyte-released gliotransmitters exerting opposing effects on the neurons: increasing the release probability of the presynaptic neuron while hyperpolarizing the post-synaptic one at a longer time scale. We simulated the joint networks with various levels of astrocyte contributions and neuronal activity levels. Our results indicate that astrocytes prolong the burst duration of neurons, while restricting hyperactivity. Thus, in our model, the effect of astrocytes is homeostatic; the firing rate of the network stabilizes to an intermediate level independently of neuronal base activity. Our computational model highlights the plausible roles of astrocytes in interconnected astrocytic and neuronal networks. Our simulations support recent findings in neurons and astrocytes in vivo and in vitro suggesting that astrocytic networks provide a modulatory role in the bursting of the neuronal network.
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Affiliation(s)
- Kerstin Lenk
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Eero Satuvuori
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Institute for Complex Systems (ISC), National Research Council (CNR), Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy.,Department of Human Movement Sciences, MOVE Research Institute Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jules Lallouette
- INRIA, Villeurbanne, France.,LIRIS UMR5205, University of Lyon, Villeurbanne, France
| | | | - Hugues Berry
- INRIA, Villeurbanne, France.,LIRIS UMR5205, University of Lyon, Villeurbanne, France
| | - Jari A K Hyttinen
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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10
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Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GMG, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife 2019; 8:e44494. [PMID: 31025934 PMCID: PMC6534378 DOI: 10.7554/elife.44494] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 04/25/2019] [Indexed: 12/22/2022] Open
Abstract
Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Benjamin A Suter
- Department of PhysiologyNorthwestern UniversityChicagoUnited States
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and PharmacologyUniversity College LondonLondonUnited Kingdom
| | | | | | - Facundo Rodriguez
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- MetaCell LLCBostonUnited States
| | - David J Kedziora
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - George L Chadderdon
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Cliff C Kerr
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - Samuel A Neymotin
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Nathan Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Robert A McDougal
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
- Center for Medical InformaticsYale UniversityNew HavenUnited States
| | - Michael Hines
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
| | | | - William W Lytton
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Department of NeurologyKings County HospitalBrooklynUnited States
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11
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Kiar G, Brown ST, Glatard T, Evans AC. A Serverless Tool for Platform Agnostic Computational Experiment Management. Front Neuroinform 2019; 13:12. [PMID: 30890927 PMCID: PMC6411646 DOI: 10.3389/fninf.2019.00012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 02/15/2019] [Indexed: 01/22/2023] Open
Abstract
Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable technical acumen, platforms, and standards have been developed to ease this burden on scientists. While web-portals make resources widely accessible, data organizations such as the Brain Imaging Data Structure and tool description languages such as Boutiques provide researchers with a foothold to tackle these problems using their own datasets, pipelines, and environments. While these standards lower the barrier to adoption of HPC and cloud systems for neuroscience applications, they still require the consolidation of disparate domain-specific knowledge. We present Clowdr, a lightweight tool to launch experiments on HPC systems and clouds, record rich execution records, and enable the accessible sharing and re-launch of experimental summaries and results. Clowdr uniquely sits between web platforms and bare-metal applications for experiment management by preserving the flexibility of do-it-yourself solutions while providing a low barrier for developing, deploying and disseminating neuroscientific analysis.
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Affiliation(s)
- Gregory Kiar
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Shawn T. Brown
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science, Concordia University, Montreal, QC, Canada
| | - Alan C. Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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12
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Chatzikonstantis G, Sidiropoulos H, Strydis C, Negrello M, Smaragdos G, De Zeeuw C, Soudris D. Multinode implementation of an extended Hodgkin–Huxley simulator. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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