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Johnsen KA, Cruzado NA, Menard ZC, Willats AA, Charles AS, Markowitz JE, Rozell CJ. Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.27.525963. [PMID: 39026717 PMCID: PMC11257437 DOI: 10.1101/2023.01.27.525963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.
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Affiliation(s)
- Kyle A. Johnsen
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Zachary C. Menard
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam A. Willats
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam S. Charles
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey E. Markowitz
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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2
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Miedema R, Strydis C. ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations. Front Neuroinform 2024; 18:1330875. [PMID: 38680548 PMCID: PMC11045893 DOI: 10.3389/fninf.2024.1330875] [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: 10/31/2023] [Accepted: 02/05/2024] [Indexed: 05/01/2024] Open
Abstract
Introduction In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions. Methods This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH. Results Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms. Discussion The ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.
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Affiliation(s)
- Rene Miedema
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Christos Strydis
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
- Quantum and Computer Engineering Department, Delft University of Technology, Delft, Netherlands
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3
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Golebiewski M, Bader G, Gleeson P, Gorochowski TE, Keating SM, König M, Myers CJ, Nickerson DP, Sommer B, Waltemath D, Schreiber F. Specifications of standards in systems and synthetic biology: status, developments, and tools in 2024. J Integr Bioinform 2024; 21:jib-2024-0015. [PMID: 39026464 DOI: 10.1515/jib-2024-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Affiliation(s)
- Martin Golebiewski
- 40092 Heidelberg Institute for Theoretical Studies (HITS) , Heidelberg, Germany
| | | | - Padraig Gleeson
- Dept. of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - Sarah M Keating
- Advanced Research Computing Centre, University College London, London, UK
| | - Matthias König
- 9373 Institute for Biology, Institute for Theoretical Biology, Humboldt-University Berlin , Berlin, Germany
| | - Chris J Myers
- Dept. of Electrical, Computer, and Energy Eng., University of Colorado Boulder, Boulder, USA
| | - David P Nickerson
- 428614 Auckland Bioengineering Institute, University of Auckland , Auckland, New Zealand
| | | | - Dagmar Waltemath
- Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
| | - Falk Schreiber
- Dept. of Computer and Information Science, 26567 University of Konstanz , Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
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4
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Wang C, Zhang T, Chen X, He S, Li S, Wu S. BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife 2023; 12:e86365. [PMID: 38132087 PMCID: PMC10796146 DOI: 10.7554/elife.86365] [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: 01/21/2023] [Accepted: 12/20/2023] [Indexed: 12/23/2023] Open
Abstract
Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit, Graphics Processing Unit, and Tensor Processing Unit, which ensures high-running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs.
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Affiliation(s)
- Chaoming Wang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Guangdong Institute of Intelligence Science and TechnologyGuangdongChina
| | - Tianqiu Zhang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Xiaoyu Chen
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Sichao He
- Beijing Jiaotong UniversityBeijingChina
| | - Shangyang Li
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Si Wu
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Guangdong Institute of Intelligence Science and TechnologyGuangdongChina
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5
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Mehta K, Ljungquist B, Ogden J, Nanda S, Ascoli RG, Ng L, Ascoli GA. Online conversion of reconstructed neural morphologies into standardized SWC format. Nat Commun 2023; 14:7429. [PMID: 37973857 PMCID: PMC10654402 DOI: 10.1038/s41467-023-42931-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconstruct neural morphology. These computer programs typically encode the data in custom file formats. The resulting format heterogeneity severely hampers the interoperability and reusability of these valuable data. Among these many alternatives, the SWC file format has emerged as a popular community choice, coalescing a rich ecosystem of related neuroinformatics resources for tracing, visualization, analysis, and simulation. This report presents a standardized specification of the SWC file format. In addition, we introduce xyz2swc, a free online service that converts all 26 reconstruction formats (and 72 variations) described in the scientific literature into the SWC standard. The xyz2swc service is available open source through a user-friendly browser interface ( https://neuromorpho.org/xyz2swc/ui/ ) and an Application Programming Interface (API).
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Affiliation(s)
- Ketan Mehta
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - James Ogden
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Ruben G Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA.
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6
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Bologna LL, Tocco A, Smiriglia R, Romani A, Schürmann F, Migliore M. Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub. Front Neuroinform 2023; 17:1271059. [PMID: 38025966 PMCID: PMC10646550 DOI: 10.3389/fninf.2023.1271059] [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: 08/01/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.
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Affiliation(s)
| | - Antonino Tocco
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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7
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Haufler D, Ito S, Koch C, Arkhipov A. Simulations of cortical networks using spatially extended conductance-based neuronal models. J Physiol 2023; 601:3123-3139. [PMID: 36567262 PMCID: PMC10290729 DOI: 10.1113/jp284030] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022] Open
Abstract
The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.
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Affiliation(s)
| | - Shinya Ito
- Mindscope Program, Allen Institute, Seattle, 98109
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8
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Keto L, Manninen T. CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations. Neuroinformatics 2023; 21:483-500. [PMID: 37133688 PMCID: PMC10406679 DOI: 10.1007/s12021-023-09627-5] [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] [Accepted: 03/06/2023] [Indexed: 05/04/2023]
Abstract
Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.
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Affiliation(s)
- Laura Keto
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
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9
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Gleeson P, Crook S, Turner D, Mantel K, Raunak M, Willke T, Cohen JD. Integrating model development across computational neuroscience, cognitive science, and machine learning. Neuron 2023; 111:1526-1530. [PMID: 37100054 PMCID: PMC7616330 DOI: 10.1016/j.neuron.2023.03.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/14/2023] [Accepted: 03/28/2023] [Indexed: 04/28/2023]
Abstract
Neuroscience, cognitive science, and computer science are increasingly benefiting through their interactions. This could be accelerated by direct sharing of computational models across disparate modeling software used in each. We describe a Model Description Format designed to meet this challenge.
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Affiliation(s)
- Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - David Turner
- Princeton Institute for Computational Science & Engineering, Princeton University, Princeton, NJ, USA
| | - Katherine Mantel
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Ted Willke
- Intel Labs, Intel Corp, Hillsboro, OR, USA
| | - Jonathan D Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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10
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Mante J, Abam J, Samineni SP, Pötzsch IM, Beal J, Myers CJ. Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa. ACS Synth Biol 2023; 12:340-346. [PMID: 36595709 DOI: 10.1021/acssynbio.2c00521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Julian Abam
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Sai P Samineni
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris J Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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11
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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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12
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Alevi D, Stimberg M, Sprekeler H, Obermayer K, Augustin M. Brian2CUDA: Flexible and Efficient Simulation of Spiking Neural Network Models on GPUs. Front Neuroinform 2022; 16:883700. [PMID: 36387586 PMCID: PMC9660315 DOI: 10.3389/fninf.2022.883700] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/09/2022] [Indexed: 03/26/2024] Open
Abstract
Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simulator with a GPU backend. Our implementation generates efficient code for the numerical integration of neuronal states and for the propagation of synaptic events on GPUs, making use of their massively parallel arithmetic capabilities. We benchmark the performance improvements of our software for several model types and find that it can accelerate simulations by up to three orders of magnitude compared to Brian's CPU backend. Currently, Brian2CUDA is the only package that supports Brian's full feature set on GPUs, including arbitrary neuron and synapse models, plasticity rules, and heterogeneous delays. When comparing its performance with Brian2GeNN, another GPU-based backend for the Brian simulator with fewer features, we find that Brian2CUDA gives comparable speedups, while being typically slower for small and faster for large networks. By combining the flexibility of the Brian simulator with the simulation speed of GPUs, Brian2CUDA enables researchers to efficiently simulate spiking neural networks with minimal effort and thereby makes the advancements of GPU computing available to a larger audience of neuroscientists.
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Affiliation(s)
- Denis Alevi
- Technische Universität Berlin, Chair of Modelling of Cognitive Processes, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Marcel Stimberg
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Henning Sprekeler
- Technische Universität Berlin, Chair of Modelling of Cognitive Processes, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Klaus Obermayer
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Technische Universität Berlin, Chair of Neural Information Processing, Berlin, Germany
| | - Moritz Augustin
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Technische Universität Berlin, Chair of Neural Information Processing, Berlin, Germany
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13
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Masoli S, Rizza MF, Tognolina M, Prestori F, D’Angelo E. Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation. Front Comput Neurosci 2022; 16:1006989. [PMID: 36387305 PMCID: PMC9649760 DOI: 10.3389/fncom.2022.1006989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | | | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- *Correspondence: Francesca Prestori,
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
- Egidio D’Angelo,
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14
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Connectivity concepts in neuronal network modeling. PLoS Comput Biol 2022; 18:e1010086. [PMID: 36074778 PMCID: PMC9455883 DOI: 10.1371/journal.pcbi.1010086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022] Open
Abstract
Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
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15
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Eriksson O, Bhalla US, Blackwell KT, Crook SM, Keller D, Kramer A, Linne ML, Saudargienė A, Wade RC, Hellgren Kotaleski J. Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife 2022; 11:e69013. [PMID: 35792600 PMCID: PMC9259018 DOI: 10.7554/elife.69013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/13/2022] [Indexed: 12/22/2022] Open
Abstract
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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Affiliation(s)
- Olivia Eriksson
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
| | - Upinder Singh Bhalla
- National Center for Biological Sciences, Tata Institute of Fundamental ResearchBangaloreIndia
| | - Kim T Blackwell
- Department of Bioengineering, Volgenau School of Engineering, George Mason UniversityFairfaxUnited States
| | - Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State UniversityTempeUnited States
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrei Kramer
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere UniversityTampereFinland
| | - Ausra Saudargienė
- Neuroscience Institute, Lithuanian University of Health SciencesKaunasLithuania
- Department of Informatics, Vytautas Magnus UniversityKaunasLithuania
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
- Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of HeidelbergHeidelbergGermany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg UniversityHeidelbergGermany
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
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16
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Kriener B, Hu H, Vervaeke K. Parvalbumin interneuron dendrites enhance gamma oscillations. Cell Rep 2022; 39:110948. [PMID: 35705055 DOI: 10.1016/j.celrep.2022.110948] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 02/07/2022] [Accepted: 05/21/2022] [Indexed: 11/24/2022] Open
Abstract
Dendrites are essential determinants of the input-output relationship of single neurons, but their role in network computations is not well understood. Here, we use a combination of dendritic patch-clamp recordings and in silico modeling to determine how dendrites of parvalbumin (PV)-expressing basket cells contribute to network oscillations in the gamma frequency band. Simultaneous soma-dendrite recordings from PV basket cells in the dentate gyrus reveal that the slope, or gain, of the dendritic input-output relationship is exceptionally low, thereby reducing the cell's sensitivity to changes in its input. By simulating gamma oscillations in detailed network models, we demonstrate that the low gain is key to increase spike synchrony in PV basket cell assemblies when cells are driven by spatially and temporally heterogeneous synaptic inputs. These results highlight the role of inhibitory neuron dendrites in synchronized network oscillations.
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Affiliation(s)
- Birgit Kriener
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Oslo, Norway
| | - Hua Hu
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Oslo, Norway
| | - Koen Vervaeke
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Oslo, Norway.
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17
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Panagiotou S, Sidiropoulos H, Soudris D, Negrello M, Strydis C. EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator. Front Neuroinform 2022; 16:724336. [PMID: 35669596 PMCID: PMC9167055 DOI: 10.3389/fninf.2022.724336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modeling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML-v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs from one to nearly two orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available.
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Affiliation(s)
- Sotirios Panagiotou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
- *Correspondence: Sotirios Panagiotou
| | - Harry Sidiropoulos
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Dimitrios Soudris
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Mario Negrello
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
- Mario Negrello
| | - Christos Strydis
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
- Quantum and Computer Engineering Department, Delft University of Technology, Delft, Netherlands
- Christos Strydis
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18
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Herbers P, Calvo I, Diaz-Pier S, Robles OD, Mata S, Toharia P, Pastor L, Peyser A, Morrison A, Klijn W. ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks. Front Neuroinform 2022; 15:766697. [PMID: 35069166 PMCID: PMC8777257 DOI: 10.3389/fninf.2021.766697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022] Open
Abstract
An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.
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Affiliation(s)
- Patrick Herbers
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Iago Calvo
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- *Correspondence: Wouter Klijn
| | - Oscar D. Robles
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Susana Mata
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Pablo Toharia
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
- DATSI, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain
| | - Luis Pastor
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alexander Peyser
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Institute of Neuroscience and Medicine and Institute for Advanced Simulation and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Sandra Diaz-Pier
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19
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Abrams MB, Bjaalie JG, Das S, Egan GF, Ghosh SS, Goscinski WJ, Grethe JS, Kotaleski JH, Ho ETW, Kennedy DN, Lanyon LJ, Leergaard TB, Mayberg HS, Milanesi L, Mouček R, Poline JB, Roy PK, Strother SC, Tang TB, Tiesinga P, Wachtler T, Wójcik DK, Martone ME. A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility. Neuroinformatics 2022; 20:25-36. [PMID: 33506383 PMCID: PMC9036053 DOI: 10.1007/s12021-020-09509-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/28/2020] [Indexed: 01/07/2023]
Abstract
There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.
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Affiliation(s)
| | - Jan G. Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Samir Das
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, QC Canada
| | - Gary F. Egan
- Monash Biomedical Imaging, Monash University, Clayton, VIC Australia
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA USA ,Department of Otolaryngology - Head and Neck Surgery Harvard Medical School Boston, Boston, MA USA
| | | | - Jeffrey S. Grethe
- Department of Neuroscience, School of Medicine, University of California, San Diego, La Jolla, CA USA
| | | | - Eric Tatt Wei Ho
- Centre for Intelligent Signal and Imaging Research, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Perak, Malaysia
| | - David N. Kennedy
- Department of Psychiatry, University of Massachusetts Medical School, Worchester, MA USA
| | | | | | - Helen S. Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine, New York, NY USA
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council (CNR), Milan, Italy
| | - Roman Mouček
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - J. B. Poline
- Montreal Neurological Institute, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Prasun K. Roy
- Computational Neuroscience & Neuroimaging Laboratory, School of Bio-Medical Engineering, Indian Institute of Technology (BHU), Varanasi, UP India
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Department of Medical Biophysics, University of Toronto, Ontario, ON Canada
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia
| | - Paul Tiesinga
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Thomas Wachtler
- Department of Biology II, Ludwig-Maximilians-Universität München, Martinsried, Planegg Germany
| | - Daniel K. Wójcik
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Maryann E. Martone
- Department of Neuroscience, School of Medicine, University of California, San Diego, La Jolla, CA USA
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20
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van der Vlag M, Woodman M, Fousek J, Diaz-Pier S, Pérez Martín A, Jirsa V, Morrison A. RateML: A Code Generation Tool for Brain Network Models. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:826345. [PMID: 36926112 PMCID: PMC10013028 DOI: 10.3389/fnetp.2022.826345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022]
Abstract
Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML's Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model's mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.
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Affiliation(s)
- Michiel van der Vlag
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Marmaduke Woodman
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Jan Fousek
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Aarón Pérez Martín
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain, Jülich, Germany.,Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany
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21
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McDougal RA, Conte C, Eggleston L, Newton AJH, Galijasevic H. Efficient Simulation of 3D Reaction-Diffusion in Models of Neurons and Networks. Front Neuroinform 2022; 16:847108. [PMID: 35655652 PMCID: PMC9152282 DOI: 10.3389/fninf.2022.847108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 04/20/2022] [Indexed: 12/20/2022] Open
Abstract
Neuronal activity is the result of both the electrophysiology and chemophysiology. A neuron can be well-represented for the purposes of electrophysiological simulation as a tree composed of connected cylinders. This representation is also apt for 1D simulations of their chemophysiology, provided the spatial scale is larger than the diameter of the cylinders and there is radial symmetry. Higher dimensional simulation is necessary to accurately capture the dynamics when these criteria are not met, such as with wave curvature, spines, or diffusion near the soma. We have developed a solution to enable efficient finite volume method simulation of reaction-diffusion kinetics in intracellular 3D regions in neuron and network models and provide an implementation within the NEURON simulator. An accelerated version of the CTNG 3D reconstruction algorithm transforms morphologies suitable for ion-channel based simulations into consistent 3D voxelized regions. Kinetics are then solved using a parallel algorithm based on Douglas-Gunn that handles the irregular 3D geometry of a neuron; these kinetics are coupled to NEURON's 1D mechanisms for ion channels, synapses, pumps, and so forth. The 3D domain may cover the entire cell or selected regions of interest. Simulations with dendritic spines and of the soma reveal details of dynamics that would be missed in a pure 1D simulation. We describe and validate the methods and discuss their performance.
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Affiliation(s)
- Robert A McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.,Center for Medical Informatics, Yale University, New Haven, CT, United States.,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
| | - Cameron Conte
- Center for Medical Informatics, Yale University, New Haven, CT, United States.,Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States.,Department of Statistics, The Ohio State University, Columbus, OH, United States
| | - Lia Eggleston
- Yale College, Yale University, New Haven, CT, United States
| | - Adam J H Newton
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.,Center for Medical Informatics, Yale University, New Haven, CT, United States.,Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, New York, NY, United States
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22
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Schreiber F, Gleeson P, Golebiewski M, Gorochowski TE, Hucka M, Keating SM, König M, Myers CJ, Nickerson DP, Sommer B, Waltemath D. Specifications of standards in systems and synthetic biology: status and developments in 2021. J Integr Bioinform 2021; 18:jib-2021-0026. [PMID: 34674411 PMCID: PMC8573232 DOI: 10.1515/jib-2021-0026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.
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Affiliation(s)
- Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
| | | | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | | | | | - Matthias König
- Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Chris J. Myers
- Department of Electrical, Computer, and Energy Eng., University of Colorado Boulder, Boulder, USA
| | - David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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23
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Bologna LL, Smiriglia R, Curreri D, Migliore M. The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data. Front Neuroinform 2021; 15:713899. [PMID: 34512300 PMCID: PMC8427185 DOI: 10.3389/fninf.2021.713899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/05/2021] [Indexed: 12/03/2022] Open
Abstract
The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed data-driven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking. To address this problem, in the framework of the Human Brain Project and the EBRAINS research infrastructure, we have developed and made available to the scientific community the NeuroFeatureExtract, an open-access online resource for the extraction of electrophysiological features from neural activity data. This tool allows to select electrophysiological traces of interest, fetched from public repositories or from users’ own data, and provides ad hoc functionalities to extract relevant features. The output files are properly formatted for further analysis, including data-driven neural model optimization.
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Affiliation(s)
- Luca L Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Dario Curreri
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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24
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Tejada J, Roque AC. Conductance-based models and the fragmentation problem: A case study based on hippocampal CA1 pyramidal cell models and epilepsy. Epilepsy Behav 2021; 121:106841. [PMID: 31864945 DOI: 10.1016/j.yebeh.2019.106841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 10/25/2022]
Abstract
Epilepsy has been a central topic in computational neuroscience, and in silico models have shown to be excellent tools to integrate and evaluate findings from animal and clinical settings. Among the different languages and tools for computational modeling development, NEURON stands out as one of the most used and mature neurosimulators. However, despite the vast quantity of models developed with NEURON, a fragmentation problem is evident in the great majority of models related to the same type of cell or cell properties. This fragmentation causes a lack of interoperability between the models because of differences in parameters. The problem is not related to the neurosimulator, which is prepared to reuse elements of other models, but related to decisions made during the model development, when it is not uncommon to adjust parameter values according to the necessities of the study. Here, this problem is presented by studying computational models related to temporal lobe epilepsy and the definitions of hippocampal CA1 pyramidal cells. The current assessment aims to highlight the implications of fragmentation for reliable modeling and the need to adopt a framework that allows a better interoperability between different models. This article is part of the Special Issue "NEWroscience 2018".
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Affiliation(s)
- Julian Tejada
- Departamento de Psicologia, DPS, Universidade Federal de Sergipe, SE 49100-000, Brazil; Facultad de Psicología, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia.
| | - Antonio C Roque
- Departamento de Física, FFCLRP, Universidade de São Paulo, Ribeirão Preto, SP 14040-901, Brazil
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25
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Gurnani H, Silver RA. Multidimensional population activity in an electrically coupled inhibitory circuit in the cerebellar cortex. Neuron 2021; 109:1739-1753.e8. [PMID: 33848473 PMCID: PMC8153252 DOI: 10.1016/j.neuron.2021.03.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 01/20/2021] [Accepted: 03/20/2021] [Indexed: 01/05/2023]
Abstract
Inhibitory neurons orchestrate the activity of excitatory neurons and play key roles in circuit function. Although individual interneurons have been studied extensively, little is known about their properties at the population level. Using random-access 3D two-photon microscopy, we imaged local populations of cerebellar Golgi cells (GoCs), which deliver inhibition to granule cells. We show that population activity is organized into multiple modes during spontaneous behaviors. A slow, network-wide common modulation of GoC activity correlates with the level of whisking and locomotion, while faster (<1 s) differential population activity, arising from spatially mixed heterogeneous GoC responses, encodes more precise information. A biologically detailed GoC circuit model reproduced the common population mode and the dimensionality observed experimentally, but these properties disappeared when electrical coupling was removed. Our results establish that local GoC circuits exhibit multidimensional activity patterns that could be used for inhibition-mediated adaptive gain control and spatiotemporal patterning of downstream granule cells.
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Affiliation(s)
- Harsha Gurnani
- Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, UK
| | - R Angus Silver
- Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, UK.
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26
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Rampadarath A, Nickerson DP. Automated Execution of Simulation Studies in Systems Medicine Using SED-ML and COMBINE Archive. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11688-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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27
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Dai K, Gratiy SL, Billeh YN, Xu R, Cai B, Cain N, Rimehaug AE, Stasik AJ, Einevoll GT, Mihalas S, Koch C, Arkhipov A. Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLoS Comput Biol 2020; 16:e1008386. [PMID: 33253147 PMCID: PMC7728187 DOI: 10.1371/journal.pcbi.1008386] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/10/2020] [Accepted: 09/16/2020] [Indexed: 11/26/2022] Open
Abstract
Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute, Seattle, Washington, United States of America
| | | | - Yazan N. Billeh
- Allen Institute, Seattle, Washington, United States of America
| | - Richard Xu
- Allen Institute, Seattle, Washington, United States of America
| | - Binghuang Cai
- Allen Institute, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute, Seattle, Washington, United States of America
| | - Atle E. Rimehaug
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | | | - Gaute T. Einevoll
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | - Stefan Mihalas
- Allen Institute, Seattle, Washington, United States of America
| | - Christof Koch
- Allen Institute, Seattle, Washington, United States of America
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
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28
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Schreiber F, Sommer B, Czauderna T, Golebiewski M, Gorochowski TE, Hucka M, Keating SM, König M, Myers C, Nickerson D, Waltemath D. Specifications of standards in systems and synthetic biology: status and developments in 2020. J Integr Bioinform 2020; 17:jib-2020-0022. [PMID: 32598316 PMCID: PMC7756620 DOI: 10.1515/jib-2020-0022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 04/27/2020] [Indexed: 12/14/2022] Open
Abstract
This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.
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Affiliation(s)
- Falk Schreiber
- Dept. of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | | | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | | | | | - Matthias König
- Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Chris Myers
- Dept. of Electrical and Computer Engineering, University of Utah, Salt Lake City, USA
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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29
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Dai K, Hernando J, Billeh YN, Gratiy SL, Planas J, Davison AP, Dura-Bernal S, Gleeson P, Devresse A, Dichter BK, Gevaert M, King JG, Van Geit WAH, Povolotsky AV, Muller E, Courcol JD, Arkhipov A. The SONATA data format for efficient description of large-scale network models. PLoS Comput Biol 2020; 16:e1007696. [PMID: 32092054 PMCID: PMC7058350 DOI: 10.1371/journal.pcbi.1007696] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/05/2020] [Accepted: 01/28/2020] [Indexed: 12/04/2022] Open
Abstract
Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility. Neuroscience is experiencing a rapid growth of data streams characterizing composition, connectivity, and activity of brain networks in ever increasing details. Data-driven modeling will be essential to integrate these multimodal and complex data into predictive simulations to advance our understanding of brain function and mechanisms. To enable efficient development and sharing of such large-scale models utilizing diverse data types, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is already supported by several popular tools for model building, simulations, and visualization. It is free and open for everyone to use and build upon and will enable increased efficiency, reproducibility, and scientific exchange in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Juan Hernando
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Yazan N. Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey L. Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Judit Planas
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience UMR, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Salvador Dura-Bernal
- State University of New York Downstate Medical Center, Brooklyn, New York, United States of America
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Adrien Devresse
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Benjamin K. Dichter
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Michael Gevaert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James G. King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Arseny V. Povolotsky
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- * E-mail:
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30
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Poldrack RA, Feingold F, Frank MJ, Gleeson P, de Hollander G, Huys QJM, Love BC, Markiewicz CJ, Moran R, Ritter P, Rogers TT, Turner BM, Yarkoni T, Zhan M, Cohen JD. The importance of standards for sharing of computational models and data. COMPUTATIONAL BRAIN & BEHAVIOR 2019; 2:229-232. [PMID: 32440654 PMCID: PMC7241435 DOI: 10.1007/s42113-019-00062-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Petra Ritter
- Charité Universitätsmedizin Berlin & Berlin Institute of Health
| | | | | | | | - Ming Zhan
- National Institute of Mental Health, NIH
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31
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Venkadesh S, Komendantov AO, Wheeler DW, Hamilton DJ, Ascoli GA. Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity. PLoS Comput Biol 2019; 15:e1007462. [PMID: 31658260 PMCID: PMC6837624 DOI: 10.1371/journal.pcbi.1007462] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 11/07/2019] [Accepted: 10/07/2019] [Indexed: 12/02/2022] Open
Abstract
Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In previous work, the comprehensive knowledge base of hippocampal neuron types Hippocampome.org systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a complete set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. The models were created using an automated pipeline based on evolutionary algorithms. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the large-scale interactions of various neuron types at the mesoscopic level.
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Affiliation(s)
- Siva Venkadesh
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - Alexander O. Komendantov
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - Diek W. Wheeler
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - David J. Hamilton
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
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32
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Stimberg M, Brette R, Goodman DFM. Brian 2, an intuitive and efficient neural simulator. eLife 2019; 8:e47314. [PMID: 31429824 PMCID: PMC6786860 DOI: 10.7554/elife.47314] [Citation(s) in RCA: 184] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 08/19/2019] [Indexed: 01/20/2023] Open
Abstract
Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.
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Affiliation(s)
- Marcel Stimberg
- Sorbonne Université, INSERM, CNRS, Institut de la VisionParisFrance
| | - Romain Brette
- Sorbonne Université, INSERM, CNRS, Institut de la VisionParisFrance
| | - Dan FM Goodman
- Department of Electrical and Electronic EngineeringImperial College LondonLondonUnited Kingdom
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33
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Gleeson P, Cantarelli M, Marin B, Quintana A, Earnshaw M, Sadeh S, Piasini E, Birgiolas J, Cannon RC, Cayco-Gajic NA, Crook S, Davison AP, Dura-Bernal S, Ecker A, Hines ML, Idili G, Lanore F, Larson SD, Lytton WW, Majumdar A, McDougal RA, Sivagnanam S, Solinas S, Stanislovas R, van Albada SJ, van Geit W, Silver RA. Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron 2019; 103:395-411.e5. [PMID: 31201122 PMCID: PMC6693896 DOI: 10.1016/j.neuron.2019.05.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 03/04/2019] [Accepted: 05/09/2019] [Indexed: 02/07/2023]
Abstract
Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community.
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Affiliation(s)
- Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Matteo Cantarelli
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; MetaCell Limited, Oxford, UK
| | - Boris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo André, Brazil
| | - Adrian Quintana
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Matt Earnshaw
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sadra Sadeh
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Eugenio Piasini
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Computational Neuroscience Initiative and Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
| | - Justas Birgiolas
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - N Alex Cayco-Gajic
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sharon Crook
- School of Life Sciences, Arizona State University, Tempe, AZ, USA; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - Andrew P Davison
- Unité de Neuroscience, Information et Complexité, Centre National de la Recherche Scientifique, Paris, France
| | | | - András Ecker
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael L Hines
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | | | - Frederic Lanore
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - William W Lytton
- SUNY Downstate Medical Center and Kings County Hospital, Brooklyn, NY, USA
| | | | - Robert A McDougal
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA; Center for Medical Informatics, Yale University, New Haven, CT, USA
| | | | - Sergio Solinas
- Department of Biomedical Science, University of Sassari, Sassari, Italy; Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Rokas Stanislovas
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sacha J van Albada
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Werner van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - R Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
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34
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Schreiber F, Sommer B, Bader GD, Gleeson P, Golebiewski M, Hucka M, Keating SM, König M, Myers C, Nickerson D, Waltemath D. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019. J Integr Bioinform 2019; 16:/j/jib.2019.16.issue-2/jib-2019-0035/jib-2019-0035.xml. [PMID: 31301675 PMCID: PMC6798822 DOI: 10.1515/jib-2019-0035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.
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Affiliation(s)
- Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of IT, Monash University, Clayton, Australia
| | - Björn Sommer
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Gary D. Bader
- The Donnelly Centre, University of Toronto, Toronto, Canada
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Michael Hucka
- California Institute of Technology, Pasadena, CA, USA
| | | | | | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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35
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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: 68] [Impact Index Per Article: 13.6] [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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36
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Neal ML, König M, Nickerson D, Mısırlı G, Kalbasi R, Dräger A, Atalag K, Chelliah V, Cooling MT, Cook DL, Crook S, de Alba M, Friedman SH, Garny A, Gennari JH, Gleeson P, Golebiewski M, Hucka M, Juty N, Myers C, Olivier BG, Sauro HM, Scharm M, Snoep JL, Touré V, Wipat A, Wolkenhauer O, Waltemath D. Harmonizing semantic annotations for computational models in biology. Brief Bioinform 2019; 20:540-550. [PMID: 30462164 PMCID: PMC6433895 DOI: 10.1093/bib/bby087] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/08/2018] [Accepted: 08/17/2018] [Indexed: 02/06/2023] Open
Abstract
Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
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Affiliation(s)
- Maxwell Lewis Neal
- Seattle Children’s Research Institute, Center for Global Infectious Disease Research, Seattle, USA
| | - Matthias König
- Department of Biology, Humboldt-University Berlin, Institute for Theoretical Biology, Berlin, Germany
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, UK
| | - Reza Kalbasi
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Vijayalakshmi Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael T Cooling
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Daniel L Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA
| | - Miguel de Alba
- German Federal Institute for Risk Assessment, Berlin, Germany
| | | | - Alan Garny
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
| | - Brett G Olivier
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Modelling of Biological Processes, BioQUANT/COS, Heidelberg University, Germany
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Jacky L Snoep
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa
- Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Manchester Institute for Biotechnology, University of Manchester, Manchester, UK
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
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37
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Stanford NJ, Scharm M, Dobson PD, Golebiewski M, Hucka M, Kothamachu VB, Nickerson D, Owen S, Pahle J, Wittig U, Waltemath D, Goble C, Mendes P, Snoep J. Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. Methods Mol Biol 2019; 2049:285-314. [PMID: 31602618 DOI: 10.1007/978-1-4939-9736-7_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.
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Affiliation(s)
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Paul D Dobson
- School of Computer Science, University of Manchester, Manchester, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Michael Hucka
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Stuart Owen
- School of Computer Science, University of Manchester, Manchester, UK
| | - Jürgen Pahle
- BIOMS/BioQuant, Heidelberg University, Heidelberg, Germany.
| | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Dagmar Waltemath
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Carole Goble
- School of Computer Science, University of Manchester, Manchester, UK
| | - Pedro Mendes
- Centre for Quantitative Medicine, University of Connecticut, Farmington, CT, USA
| | - Jacky Snoep
- School of Computer Science, University of Manchester, Manchester, UK.,Biochemistry, Stellenbosch University, Stellenbosch, South Africa
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38
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Hagen E, Næss S, Ness TV, Einevoll GT. Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front Neuroinform 2018; 12:92. [PMID: 30618697 PMCID: PMC6305460 DOI: 10.3389/fninf.2018.00092] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 11/21/2018] [Indexed: 11/13/2022] Open
Abstract
Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.
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Affiliation(s)
- Espen Hagen
- Department of Physics, University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Solveig Næss
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T Einevoll
- Department of Physics, University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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39
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Miłkowski M, Hensel WM, Hohol M. Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail. J Comput Neurosci 2018; 45:163-172. [PMID: 30377880 PMCID: PMC6306493 DOI: 10.1007/s10827-018-0702-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 10/05/2018] [Accepted: 10/17/2018] [Indexed: 01/25/2023]
Abstract
Replicability and reproducibility of computational models has been somewhat understudied by "the replication movement." In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code.
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Affiliation(s)
- Marcin Miłkowski
- Institute of Philosophy and Sociology, Polish Academy of Sciences, Nowy Świat 72, 00-330, Warsaw, Poland
| | - Witold M Hensel
- Faculty of History and Sociology, University of Białystok, Plac NZS 1, 15-420, Białystok, Poland
| | - Mateusz Hohol
- Copernicus Center for Interdisciplinary Studies, Jagiellonian University, Szczepańska 1/5, 31-011, Kraków, Poland.
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40
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Epelde G, Morgan F, Mujika A, Callaly F, Leškovský P, McGinley B, Álvarez R, Blau A, Krewer F. Web-Based Interfaces for Virtual C. elegans Neuron Model Definition, Network Configuration, Behavioral Experiment Definition and Experiment Results Visualization. Front Neuroinform 2018; 12:80. [PMID: 30483089 PMCID: PMC6243129 DOI: 10.3389/fninf.2018.00080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 10/22/2018] [Indexed: 11/29/2022] Open
Abstract
The Si elegans platform targets the complete virtualization of the nematode Caenorhabditis elegans, and its environment. This paper presents a suite of unified web-based Graphical User Interfaces (GUIs) as the main user interaction point, and discusses their underlying technologies and methods. The user-friendly features of this tool suite enable users to graphically create neuron and network models, and behavioral experiments, without requiring knowledge of domain-specific computer-science tools. The framework furthermore allows the graphical visualization of all simulation results using a worm locomotion and neural activity viewer. Models, experiment definitions and results can be exported in a machine-readable format, thereby facilitating reproducible and cross-platform execution of in silico C. elegans experiments in other simulation environments. This is made possible by a novel XML-based behavioral experiment definition encoding format, a NeuroML XML-based model generation and network configuration description language, and their associated GUIs. User survey data confirms the platform usability and functionality, and provides insights into future directions for web-based simulation GUIs of C. elegans and other living organisms. The tool suite is available online to the scientific community and its source code has been made available.
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Affiliation(s)
- Gorka Epelde
- Vicomtech, San Sebastián, Spain
- IIS Biodonostia, San Sebastián, Spain
| | - Fearghal Morgan
- Bio-Inspired Electronics and Reconfigurable Computing (BIRC) Research Group, Electrical & Electronic Engineering Department, College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland
| | | | - Frank Callaly
- Bio-Inspired Electronics and Reconfigurable Computing (BIRC) Research Group, Electrical & Electronic Engineering Department, College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland
| | | | - Brian McGinley
- Bio-Inspired Electronics and Reconfigurable Computing (BIRC) Research Group, Electrical & Electronic Engineering Department, College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland
| | - Roberto Álvarez
- Vicomtech, San Sebastián, Spain
- IIS Biodonostia, San Sebastián, Spain
| | - Axel Blau
- Department of Neuroscience and Brain Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Finn Krewer
- Bio-Inspired Electronics and Reconfigurable Computing (BIRC) Research Group, Electrical & Electronic Engineering Department, College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland
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41
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Blundell I, Brette R, Cleland TA, Close TG, Coca D, Davison AP, Diaz-Pier S, Fernandez Musoles C, Gleeson P, Goodman DFM, Hines M, Hopkins MW, Kumbhar P, Lester DR, Marin B, Morrison A, Müller E, Nowotny T, Peyser A, Plotnikov D, Richmond P, Rowley A, Rumpe B, Stimberg M, Stokes AB, Tomkins A, Trensch G, Woodman M, Eppler JM. Code Generation in Computational Neuroscience: A Review of Tools and Techniques. Front Neuroinform 2018; 12:68. [PMID: 30455637 PMCID: PMC6230720 DOI: 10.3389/fninf.2018.00068] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 09/12/2018] [Indexed: 01/18/2023] Open
Abstract
Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.
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Affiliation(s)
- Inga Blundell
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich, Germany
| | - Romain Brette
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Thomas A. Cleland
- Department of Psychology, Cornell University, Ithaca, NY, United States
| | - Thomas G. Close
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Daniel Coca
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Andrew P. Davison
- Unité de Neurosciences, Information et Complexité, CNRS FRE 3693, Gif sur Yvette, France
| | - Sandra Diaz-Pier
- Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich, Germany
| | - Carlos Fernandez Musoles
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Dan F. M. Goodman
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Michael Hines
- Department of Neurobiology, School of Medicine, Yale University, New Haven, CT, United States
| | - Michael W. Hopkins
- Advanced Processor Technologies Group, School of Computer ScienceUniversity of Manchester, Manchester, United Kingdom
| | - Pramod Kumbhar
- Blue Brain Project, EPFLCampus Biotech, Geneva, Switzerland
| | - David R. Lester
- Advanced Processor Technologies Group, School of Computer ScienceUniversity of Manchester, Manchester, United Kingdom
| | - Bóris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
- Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Abigail Morrison
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich, Germany
- Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich, Germany
- Faculty of Psychology, Institute of Cognitive NeuroscienceRuhr-University Bochum, Bochum, Germany
| | - Eric Müller
- Kirchhoff-Institute for PhysicsUniversität Heidelberg, Heidelberg, Germany
| | - Thomas Nowotny
- Centre for Computational Neuroscience and Robotics, School of Engineering and InformaticsUniversity of Sussex, Brighton, United Kingdom
| | - Alexander Peyser
- Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich, Germany
| | - Dimitri Plotnikov
- Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich, Germany
- RWTH Aachen University, Software EngineeringJülich Aachen Research Alliance, Aachen, Germany
| | - Paul Richmond
- Department of Computer ScienceUniversity of Sheffield, Sheffield, United Kingdom
| | - Andrew Rowley
- Advanced Processor Technologies Group, School of Computer ScienceUniversity of Manchester, Manchester, United Kingdom
| | - Bernhard Rumpe
- RWTH Aachen University, Software EngineeringJülich Aachen Research Alliance, Aachen, Germany
| | - Marcel Stimberg
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Alan B. Stokes
- Advanced Processor Technologies Group, School of Computer ScienceUniversity of Manchester, Manchester, United Kingdom
| | - Adam Tomkins
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Guido Trensch
- Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich, Germany
| | - Marmaduke Woodman
- Institut de Neurosciences des SystèmesAix Marseille Université, Marseille, France
| | - Jochen Martin Eppler
- Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich, Germany
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42
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Sarma GP, Lee CW, Portegys T, Ghayoomie V, Jacobs T, Alicea B, Cantarelli M, Currie M, Gerkin RC, Gingell S, Gleeson P, Gordon R, Hasani RM, Idili G, Khayrulin S, Lung D, Palyanov A, Watts M, Larson SD. OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans. Philos Trans R Soc Lond B Biol Sci 2018; 373:rstb.2017.0382. [PMID: 30201845 PMCID: PMC6158220 DOI: 10.1098/rstb.2017.0382] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2018] [Indexed: 01/02/2023] Open
Abstract
The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.
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Affiliation(s)
- Gopal P Sarma
- School of Medicine, Emory University, Atlanta, GA, USA
| | | | | | - Vahid Ghayoomie
- Laboratory of Systems Biology and Bioinformatics, University of Tehran, Tehran, Iran
| | | | | | | | - Michael Currie
- Fling Inc., Bangkok, Thailand.,Raytheon Company, Waltham, MA, USA
| | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Richard Gordon
- Embryogenesis Center, Gulf Specimen Marine Laboratory, Panacea, FL, USA.,C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA
| | - Ramin M Hasani
- Cyber-Physical Systems, Technische Universität Wien, Wien, Austria
| | | | - Sergey Khayrulin
- The OpenWorm Foundation, New York, NY, USA.,Laboratory of Complex Systems Simulation, A.P. Ershov Institute of Informatics Systems, Novosibirsk, Russia.,Laboratory of Structural Bioinformatics and Molecular Modeling, Novosibirsk State University, Novosibirsk, Russia
| | - David Lung
- Cyber-Physical Systems, Technische Universität Wien, Wien, Austria
| | - Andrey Palyanov
- Laboratory of Complex Systems Simulation, A.P. Ershov Institute of Informatics Systems, Novosibirsk, Russia.,Laboratory of Structural Bioinformatics and Molecular Modeling, Novosibirsk State University, Novosibirsk, Russia
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43
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Gleeson P, Lung D, Grosu R, Hasani R, Larson SD. c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans. Philos Trans R Soc Lond B Biol Sci 2018; 373:rstb.2017.0379. [PMID: 30201842 PMCID: PMC6158223 DOI: 10.1098/rstb.2017.0379] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2018] [Indexed: 01/03/2023] Open
Abstract
The OpenWorm project has the ambitious goal of producing a highly detailed in silico model of the nematode Caenorhabditis elegans. A crucial part of this work will be a model of the nervous system encompassing all known cell types and connections. The appropriate level of biophysical detail required in the neuronal model to reproduce observed high-level behaviours in the worm has yet to be determined. For this reason, we have developed a framework, c302, that allows different instances of neuronal networks to be generated incorporating varying levels of anatomical and physiological detail, which can be investigated and refined independently or linked to other tools developed in the OpenWorm modelling toolchain. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
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Affiliation(s)
- Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - David Lung
- Cyber-Physical Systems, Technische Universität Wien, Vienna, Austria
| | - Radu Grosu
- Cyber-Physical Systems, Technische Universität Wien, Vienna, Austria
| | - Ramin Hasani
- Cyber-Physical Systems, Technische Universität Wien, Vienna, Austria
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44
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Cantarelli M, Marin B, Quintana A, Earnshaw M, Court R, Gleeson P, Dura-Bernal S, Silver RA, Idili G. Geppetto: a reusable modular open platform for exploring neuroscience data and models. Philos Trans R Soc Lond B Biol Sci 2018; 373:rstb.2017.0380. [PMID: 30201843 PMCID: PMC6158222 DOI: 10.1098/rstb.2017.0380] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2018] [Indexed: 11/25/2022] Open
Abstract
Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications, including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for Drosophila melanogaster neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data. Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way. The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly customizable frontend. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
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Affiliation(s)
- Matteo Cantarelli
- OpenWorm Foundation, USA .,MetaCell Limited, UK.,Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | - Boris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK.,Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Brazil
| | - Adrian Quintana
- MetaCell Limited, UK.,Department of Neuroscience, Physiology and Pharmacology, University College London, UK.,EyeSeeTea Limited, UK
| | - Matt Earnshaw
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | - Robert Court
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | | | - R Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
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45
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Jȩdrzejewski-Szmek Z, Abrahao KP, Jȩdrzejewska-Szmek J, Lovinger DM, Blackwell KT. Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes. Front Neuroinform 2018; 12:47. [PMID: 30108495 PMCID: PMC6079282 DOI: 10.3389/fninf.2018.00047] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/06/2018] [Indexed: 11/25/2022] Open
Abstract
Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. Optimizations reached convergence within 1,600-4,000 model evaluations (200-500 generations × population size of 8). Analysis of the parameters of the best fitting models revealed differences between neuron subtypes, which are consistent with prior experimental results. Overall our results suggest that this easy-to-use, automatic approach for finding neuron channel parameters may be applied to current clamp recordings from neurons exhibiting different biochemical markers to help characterize ionic differences between other neuron subtypes.
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Affiliation(s)
| | - Karina P. Abrahao
- Laboratory for Integrative Neuroscience, Section on Synaptic Pharmacology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Rockville, MD, United States
| | | | - David M. Lovinger
- Laboratory for Integrative Neuroscience, Section on Synaptic Pharmacology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Rockville, MD, United States
| | - Kim T. Blackwell
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States
- Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, United States
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46
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Nair M, Manchan Kannimoola J, Jayaraman B, Nair B, Diwakar S. Temporal constrained objects for modelling neuronal dynamics. PeerJ Comput Sci 2018; 4:e159. [PMID: 33816812 PMCID: PMC7924700 DOI: 10.7717/peerj-cs.159] [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: 03/02/2018] [Accepted: 06/26/2018] [Indexed: 06/12/2023]
Abstract
BACKGROUND Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. METHODS In this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. RESULTS We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. DISCUSSION Temporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.
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Affiliation(s)
- Manjusha Nair
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
- Department of Computer Science and Applications, Amritapuri Campus, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
| | - Jinesh Manchan Kannimoola
- Center for Cybersecurity Systems and Networks, Amritapuri Campus, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
| | - Bharat Jayaraman
- Department of Computer Science & Engineering, State University of New York at Buffalo, Buffalo, NY, USA
| | - Bipin Nair
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
| | - Shyam Diwakar
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
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47
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Viswan NA, HarshaRani GV, Stefan MI, Bhalla US. FindSim: A Framework for Integrating Neuronal Data and Signaling Models. Front Neuroinform 2018; 12:38. [PMID: 29997492 PMCID: PMC6028806 DOI: 10.3389/fninf.2018.00038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 06/05/2018] [Indexed: 12/30/2022] Open
Abstract
Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model. We use a structured format for encoding the conditions of many standard physiological and pharmacological experiments, specifying which parts of the model are involved, and comparing experiment outcomes with model output. A database of such experiments is run against successive generations of composite cellular models to iteratively improve the model against each experiment, while retaining global model validity. We suggest that this toolchain provides a principled and scalable way to tackle model complexity and diversity of data sources.
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Affiliation(s)
- Nisha A Viswan
- National Centre for Biological Sciences, Bangalore, India.,Tata Institute of Fundamental Research, The University of Trans-Disciplinary Health Sciences and Technology, Bangalore, India
| | | | - Melanie I Stefan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,ZJU-UoE Institute, Zhejiang University, Hangzhou, China
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Metzner C, Mäki-Marttunen T, Zurowski B, Steuber V. Modules for Automated Validation and Comparison of Models of
Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders:
ASSRUnit—A Case Study. COMPUTATIONAL PSYCHIATRY 2018. [DOI: 10.1162/cpsy_a_00015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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49
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Manninen T, Aćimović J, Havela R, Teppola H, Linne ML. Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures. Front Neuroinform 2018; 12:20. [PMID: 29765315 PMCID: PMC5938413 DOI: 10.3389/fninf.2018.00020] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 04/06/2018] [Indexed: 01/26/2023] Open
Abstract
The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.
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Affiliation(s)
- Tiina Manninen
- Computational Neuroscience Group, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Jugoslava Aćimović
- Computational Neuroscience Group, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Riikka Havela
- Computational Neuroscience Group, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Heidi Teppola
- Computational Neuroscience Group, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Marja-Leena Linne
- Computational Neuroscience Group, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland
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50
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Mulugeta L, Drach A, Erdemir A, Hunt CA, Horner M, Ku JP, Myers JG, Vadigepalli R, Lytton WW. Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. Front Neuroinform 2018; 12:18. [PMID: 29713272 PMCID: PMC5911506 DOI: 10.3389/fninf.2018.00018] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/29/2018] [Indexed: 12/27/2022] Open
Abstract
Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.
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Affiliation(s)
| | - Andrew Drach
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - C A Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | | | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Jerry G Myers
- NASA Glenn Research Center, Cleveland, OH, United States
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics and Computational Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - William W Lytton
- Department of Neurology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Physiology and Pharmacology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Neurology, Kings County Hospital Center, New York, NY, United States
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