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Dura-Bernal S, Herrera B, Lupascu C, Marsh BM, Gandolfi D, Marasco A, Neymotin S, Romani A, Solinas S, Bazhenov M, Hay E, Migliore M, Reinmann M, Arkhipov A. Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons. J Neurosci 2024; 44:e1236242024. [PMID: 39358017 PMCID: PMC11450527 DOI: 10.1523/jneurosci.1236-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/09/2024] [Accepted: 07/31/2024] [Indexed: 10/04/2024] Open
Abstract
Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.
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Affiliation(s)
- Salvador Dura-Bernal
- State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, New York 11203
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | | | - Carmen Lupascu
- Institute of Biophysics, National Research Council/Human Brain Project, Palermo 90146, Italy
| | - Brianna M Marsh
- University of California San Diego, La Jolla, California 92093
| | - Daniela Gandolfi
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | | | - Samuel Neymotin
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
- School of Medicine, New York University, New York 10012
| | - Armando Romani
- Swiss Federal Institute of Technology Lausanne (EPFL)/Blue Brain Project, Lausanne 1015, Switzerland
| | | | - Maxim Bazhenov
- University of California San Diego, La Jolla, California 92093
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
- University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Michele Migliore
- Institute of Biophysics, National Research Council/Human Brain Project, Palermo 90146, Italy
| | - Michael Reinmann
- Swiss Federal Institute of Technology Lausanne (EPFL)/Blue Brain Project, Lausanne 1015, Switzerland
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Pierré A, Pham T, Pearl J, Datta SR, Ritt JT, Fleischmann A. A Perspective on Neuroscience Data Standardization with Neurodata Without Borders. J Neurosci 2024; 44:e0381242024. [PMID: 39293939 PMCID: PMC11411583 DOI: 10.1523/jneurosci.0381-24.2024] [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: 02/27/2024] [Revised: 07/24/2024] [Accepted: 07/30/2024] [Indexed: 09/20/2024] Open
Abstract
Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science. Key takeaways from this article are that (1) standardization with NWB requires non-trivial design choices; (2) the general practice of standardization in the lab promotes data awareness and literacy, and improves transparency, rigor, and reproducibility in our science; (3) we offer several feature suggestions to ease the extensibility, publishing/sharing, and usability for NWB standard and users of NWB data.
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Affiliation(s)
- Andrea Pierré
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island 02912
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912
| | - Tuan Pham
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island 02912
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912
| | - Jonah Pearl
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115
| | | | - Jason T Ritt
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912
| | - Alexander Fleischmann
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island 02912
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912
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3
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Makarov R, Chavlis S, Poirazi P. DendroTweaks: An interactive approach for unraveling dendritic dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.06.611191. [PMID: 39314451 PMCID: PMC11418972 DOI: 10.1101/2024.09.06.611191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks - a toolbox designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks is tailored to interactive fine-tuning of subcellular properties, such as kinetics and distributions of ion channels, as well as the dynamics and allocation of synaptic inputs. It offers an automated approach for standardization and refinement of voltage-gated ion channel models to make them more comprehensible and reusable. The toolbox allows users to run various experimental protocols and record data from multiple dendritic and somatic locations, thereby enhancing model validation. Finally, it aims to deepen our understanding of which dendritic properties are essential for neuronal input-output transformation. Using this knowledge, one can simplify models through a built-in morphology reduction algorithm and export them for further use in faster, more interpretable networks. With DendroTweaks, users can gain better control and understanding of their models, advancing research on dendritic input-output transformations and their role in network computations.
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Affiliation(s)
- Roman Makarov
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, 70013, Greece
- Department of Biology, University of Crete, Heraklion, 70013, Greece
| | - Spyridon Chavlis
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, 70013, Greece
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, 70013, Greece
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4
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Ramaswamy S. Data-driven multiscale computational models of cortical and subcortical regions. Curr Opin Neurobiol 2024; 85:102842. [PMID: 38320453 DOI: 10.1016/j.conb.2024.102842] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.
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Affiliation(s)
- Srikanth Ramaswamy
- Neural Circuits Laboratory, Biosciences Institute, Newcastle University, Newcastle Upon Tyne, NE2 4HH, United Kingdom.
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5
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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: 2] [Impact Index Per Article: 2.0] [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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6
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Dura-Bernal S, Griffith EY, Barczak A, O'Connell MN, McGinnis T, Moreira JVS, Schroeder CE, Lytton WW, Lakatos P, Neymotin SA. Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell Rep 2023; 42:113378. [PMID: 37925640 PMCID: PMC10727489 DOI: 10.1016/j.celrep.2023.113378] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/05/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Erica Y Griffith
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University Medical Center, New York, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Kings County Hospital Center, Brooklyn, NY, USA
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
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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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Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep. Cell Rep 2023; 42:112200. [PMID: 36867532 PMCID: PMC10066598 DOI: 10.1016/j.celrep.2023.112200] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 01/04/2023] [Accepted: 02/15/2023] [Indexed: 03/04/2023] Open
Abstract
Thalamoreticular circuitry plays a key role in arousal, attention, cognition, and sleep spindles, and is linked to several brain disorders. A detailed computational model of mouse somatosensory thalamus and thalamic reticular nucleus has been developed to capture the properties of over 14,000 neurons connected by 6 million synapses. The model recreates the biological connectivity of these neurons, and simulations of the model reproduce multiple experimental findings in different brain states. The model shows that inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness. We find that thalamic interactions are responsible for the characteristic waxing and waning of spindle oscillations. In addition, we find that changes in thalamic excitability control spindle frequency and their incidence. The model is made openly available to provide a new tool for studying the function and dysfunction of the thalamoreticular circuitry in various brain states.
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9
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Rübel O, Tritt A, Ly R, Dichter BK, Ghosh S, Niu L, Baker P, Soltesz I, Ng L, Svoboda K, Frank L, Bouchard KE. The Neurodata Without Borders ecosystem for neurophysiological data science. eLife 2022; 11:e78362. [PMID: 36193886 PMCID: PMC9531949 DOI: 10.7554/elife.78362] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/13/2022] [Indexed: 01/21/2023] Open
Abstract
The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.
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Affiliation(s)
- Oliver Rübel
- Scientific Data Division, Lawrence Berkeley National LaboratoryBerkeleyUnited States
| | - Andrew Tritt
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyUnited States
| | - Ryan Ly
- Scientific Data Division, Lawrence Berkeley National LaboratoryBerkeleyUnited States
| | | | - Satrajit Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical SchoolBostonUnited States
| | | | - Pamela Baker
- Allen Institute for Brain ScienceSeattleUnited States
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford UniversityStanfordUnited States
| | - Lydia Ng
- Allen Institute for Brain ScienceSeattleUnited States
| | - Karel Svoboda
- Allen Institute for Brain ScienceSeattleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Loren Frank
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Kavli Institute for Fundamental NeuroscienceSan FranciscoUnited States
- Departments of Physiology and Psychiatry University of California, San FranciscoSan FranciscoUnited States
| | - Kristofer E Bouchard
- Scientific Data Division, Lawrence Berkeley National LaboratoryBerkeleyUnited States
- Kavli Institute for Fundamental NeuroscienceSan FranciscoUnited States
- Biological Systems and Engineering Division, Lawrence Berkeley National LaboratoryBerkeleyUnited States
- Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California, BerkeleyBerkeleyUnited States
- Weill NeurohubBerkeleyUnited States
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10
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Borges FS, Moreira JVS, Takarabe LM, Lytton WW, Dura-Bernal S. Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE. Front Neuroinform 2022; 16:884245. [PMID: 36213546 PMCID: PMC9536213 DOI: 10.3389/fninf.2022.884245] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015, the Blue Brain Project (BBP) developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly detailed and complex S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model preserved the original cell morphologies, electrophysiological responses and spatial distribution for all 207 cell types; and the connectivity properties of all 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological firing rates and activity patterns across all populations. When STP was included, the network generated a 1 Hz oscillation comparable to the original model in vitro-like state. By then reducing the extracellular calcium concentration, the model reproduced the original S1 in vivo-like states with asynchronous activity. These results validate the original study using a new modeling tool. Simulated local field potentials (LFPs) exhibited realistic oscillatory patterns and features, including distance- and frequency-dependent attenuation. The model was extended by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical (TC) and corticothalamic connectivity derived from experimental data. The thalamic model reproduced single known cell and circuit-level dynamics, including burst and tonic firing modes and oscillatory patterns, providing a more realistic input to cortex and enabling study of TC interactions. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory TC circuits that can be employed as a community tool for researchers to study neural dynamics, function and disease.
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Affiliation(s)
- Fernando S. Borges
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Paulo, Brazil
| | - Joao V. S. Moreira
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
| | - Lavinia M. Takarabe
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Paulo, Brazil
| | - William W. Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Neurology, Kings County Hospital Center, Brooklyn, NY, United States
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, United States
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
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11
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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: 12] [Impact Index Per Article: 6.0] [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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12
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Schürmann F, Courcol JD, Ramaswamy S. Computational Concepts for Reconstructing and Simulating Brain Tissue. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:237-259. [PMID: 35471542 DOI: 10.1007/978-3-030-89439-9_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Affiliation(s)
- Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland.
| | - Jean-Denis Courcol
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
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13
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Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinformatics 2021; 19:685-701. [PMID: 34282528 PMCID: PMC8566446 DOI: 10.1007/s12021-021-09531-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 12/25/2022]
Abstract
Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.
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14
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Lazar AA, Liu T, Turkcan MK, Zhou Y. Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era. eLife 2021; 10:e62362. [PMID: 33616035 PMCID: PMC8016480 DOI: 10.7554/elife.62362] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 02/21/2021] [Indexed: 11/25/2022] Open
Abstract
In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.
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Affiliation(s)
- Aurel A Lazar
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
| | - Tingkai Liu
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
| | | | - Yiyin Zhou
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
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15
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Sáray S, Rössert CA, Appukuttan S, Migliore R, Vitale P, Lupascu CA, Bologna LL, Van Geit W, Romani A, Davison AP, Muller E, Freund TF, Káli S. HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLoS Comput Biol 2021; 17:e1008114. [PMID: 33513130 PMCID: PMC7875359 DOI: 10.1371/journal.pcbi.1008114] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 02/10/2021] [Accepted: 12/24/2020] [Indexed: 11/19/2022] Open
Abstract
Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.
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Affiliation(s)
- Sára Sáray
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
- * E-mail: (SS); (SK)
| | - Christian A. Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Shailesh Appukuttan
- Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Rosanna Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Paola Vitale
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Neurosciences, Faculty of Medicine, University of Montreal, Montreal, Canada
- CHU Sainte-Justine Research Center, Montreal, Canada
- Quebec Artificial Intelligence Institute (Mila), Montreal, Canada
| | - Tamás F. Freund
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
| | - Szabolcs Káli
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
- * E-mail: (SS); (SK)
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16
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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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17
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Ježek P, Teeters JL, Sommer FT. NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format. Front Neuroinform 2020; 14:27. [PMID: 33041776 PMCID: PMC7526650 DOI: 10.3389/fninf.2020.00027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 05/20/2020] [Indexed: 11/19/2022] Open
Abstract
The Neurodata Without Borders (abbreviation NWB) format is a current technology for storing neurophysiology data along with the associated metadata. Data stored in the format is organized into separate HDF5 files, each file usually storing the data associated with a single recording session. While the NWB format provides a structured method for storing data, so far there have not been tools which enable searching a collection of NWB files in order to find data of interest for a particular purpose. We describe here three tools to enable searching NWB files. The tools have different features making each of them most useful for a particular task. The first tool, called the NWB Query Engine, is written in Java. It allows searching the complete content of NWB files. It was designed for the first version of NWB (NWB 1) and supports most (but not all) features of the most recent version (NWB 2). For some searches, it is the fastest tool. The second tool, called “search_nwb” is written in Python and also allow searching the complete contents of NWB files. It works with both NWB 1 and NWB 2, as does the third tool. The third tool, called “nwbindexer” enables searching a collection of NWB files using a two-step process. In the first step, a utility is run which creates an SQLite database containing the metadata in a collection of NWB files. This database is then searched in the second step, using another utility. Once the index is built, this two-step processes allows faster searches than are done by the other tools, but does not enable as complete of searches. All three tools use a simple query language which was developed for this project. Software integrating the three tools into a web-interface is provided which enables searching NWB files by submitting a web form.
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Affiliation(s)
- Petr Ježek
- Faculty of Applied Sciences, New Technologies for the Information Society, University of West Bohemia, Plzeň, Czechia
| | - Jeffery L Teeters
- Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Friedrich T Sommer
- Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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18
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Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron 2020; 106:388-403.e18. [DOI: 10.1016/j.neuron.2020.01.040] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/17/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023]
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19
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Hjorth JJJ, Kozlov A, Carannante I, Frost Nylén J, Lindroos R, Johansson Y, Tokarska A, Dorst MC, Suryanarayana SM, Silberberg G, Hellgren Kotaleski J, Grillner S. The microcircuits of striatum in silico. Proc Natl Acad Sci U S A 2020; 117:9554-9565. [PMID: 32321828 PMCID: PMC7197017 DOI: 10.1073/pnas.2000671117] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, soma-dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.
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Affiliation(s)
- J J Johannes Hjorth
- Science for Life Laboratory, School of Electrical Engeneering and Computer Science, Royal Institute of Technology, SE-10044 Stockholm, Sweden
| | - Alexander Kozlov
- Science for Life Laboratory, School of Electrical Engeneering and Computer Science, Royal Institute of Technology, SE-10044 Stockholm, Sweden
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | - Ilaria Carannante
- Science for Life Laboratory, School of Electrical Engeneering and Computer Science, Royal Institute of Technology, SE-10044 Stockholm, Sweden
| | | | - Robert Lindroos
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | - Yvonne Johansson
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | - Anna Tokarska
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | - Matthijs C Dorst
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | | | - Gilad Silberberg
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engeneering and Computer Science, Royal Institute of Technology, SE-10044 Stockholm, Sweden;
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
| | - Sten Grillner
- Department of Neuroscience, Karolinska Institutet, SE-17165 Stockholm
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