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Li X, Liang H. Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics". Front Neuroinform 2022; 16:902452. [PMID: 36225654 PMCID: PMC9549929 DOI: 10.3389/fninf.2022.902452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.
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Affiliation(s)
- Xin Li
- School of Information Science and Technology, University of Science and Technology of China, Hefei, China
| | - Huadong Liang
- AI Research Institute, iFLYTEK Co., LTD, Hefei, China
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2
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A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits. Sci Rep 2022; 12:13864. [PMID: 35974119 PMCID: PMC9381785 DOI: 10.1038/s41598-022-18024-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 08/03/2022] [Indexed: 01/03/2023] Open
Abstract
The modeling of extended microcircuits is emerging as an effective tool to simulate the neurophysiological correlates of brain activity and to investigate brain dysfunctions. However, for specific networks, a realistic modeling approach based on the combination of available physiological, morphological and anatomical data is still an open issue. One of the main problems in the generation of realistic networks lies in the strategy adopted to build network connectivity. Here we propose a method to implement a neuronal network at single cell resolution by using the geometrical probability volumes associated with pre- and postsynaptic neurites. This allows us to build a network with plausible connectivity properties without the explicit use of computationally intensive touch detection algorithms using full 3D neuron reconstructions. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks at single cell resolution that are in good agreement with experimental findings. This geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas facilitating the simulation of different brain regions with realistic models.
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3
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Galindo SE, Toharia P, Robles OD, Pastor L. SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies. Front Neuroinform 2022; 15:753997. [PMID: 35027889 PMCID: PMC8751653 DOI: 10.3389/fninf.2021.753997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 11/22/2021] [Indexed: 12/02/2022] Open
Abstract
Brain complexity has traditionally fomented the division of neuroscience into somehow separated compartments; the coexistence of the anatomical, physiological, and connectomics points of view is just a paradigmatic example of this situation. However, there are times when it is important to combine some of these standpoints for getting a global picture, like for fully analyzing the morphological and topological features of a specific neuronal circuit. Within this framework, this article presents SynCoPa, a tool designed for bridging gaps among representations by providing techniques that allow combining detailed morphological neuron representations with the visualization of neuron interconnections at the synapse level. SynCoPa has been conceived for the interactive exploration and analysis of the connectivity elements and paths of simple to medium complexity neuronal circuits at the connectome level. This has been done by providing visual metaphors for synapses and interconnection paths, in combination with the representation of detailed neuron morphologies. SynCoPa could be helpful, for example, for establishing or confirming a hypothesis about the spatial distributions of synapses, or for answering questions about the way neurons establish connections or the relationships between connectivity and morphological features. Last, SynCoPa is easily extendable to include functional data provided, for example, by any of the morphologically-detailed simulators available nowadays, such as Neuron and Arbor, for providing a deep insight into the circuits features prior to simulating it, in particular any analysis where it is important to combine morphology, network topology, and physiology.
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Affiliation(s)
- Sergio E Galindo
- Ciencias de la Computación, Arquitectura de Computado, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Esc. Tec. Sup. de Ingeniería Informática, Rey Juan Carlos University, Madrid, Spain
| | - Pablo Toharia
- DATSI, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Oscar D Robles
- Ciencias de la Computación, Arquitectura de Computado, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Esc. Tec. Sup. de Ingeniería Informática, Rey Juan Carlos University, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Luis Pastor
- Ciencias de la Computación, Arquitectura de Computado, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Esc. Tec. Sup. de Ingeniería Informática, Rey Juan Carlos University, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
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Spreizer S, Senk J, Rotter S, Diesmann M, Weyers B. NEST Desktop, an Educational Application for Neuroscience. eNeuro 2021; 8:ENEURO.0274-21.2021. [PMID: 34764188 PMCID: PMC8638679 DOI: 10.1523/eneuro.0274-21.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/20/2021] [Accepted: 09/19/2021] [Indexed: 11/21/2022] Open
Abstract
Simulation software for spiking neuronal network models matured in the past decades regarding performance and flexibility. But the entry barrier remains high for students and early career scientists in computational neuroscience since these simulators typically require programming skills and a complex installation. Here, we describe an installation-free Graphical User Interface (GUI) running in the web browser, which is distinct from the simulation engine running anywhere, on the student's laptop or on a supercomputer. This architecture provides robustness against technological changes in the software stack and simplifies deployment for self-education and for teachers. Our new open-source tool, NEST Desktop, comprises graphical elements for creating and configuring network models, running simulations, and visualizing and analyzing the results. NEST Desktop allows students to explore important concepts in computational neuroscience without the need to learn a simulator control language before. Our experiences so far highlight that NEST Desktop helps advancing both quality and intensity of teaching in computational neuroscience in regular university courses. We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities.
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Affiliation(s)
- Sebastian Spreizer
- Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance (JARA)-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
- Department of Computer Science, University of Trier, 54296 Trier, Germany
| | - Johanna Senk
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance (JARA)-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
| | - Stefan Rotter
- Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance (JARA)-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52074 Aachen, Germany
- Department of Physics, Faculty 1, Rheinisch-Westfälische Technische Hochschule Aachen University, 52074 Aachen, Germany
| | - Benjamin Weyers
- Department of Computer Science, University of Trier, 54296 Trier, Germany
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Galindo SE, Toharia P, Robles ÓD, Ros E, Pastor L, Garrido JA. Simulation, visualization and analysis tools for pattern recognition assessment with spiking neuronal networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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6
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Aliaga Maraver JJ, Mata S, Benavides-Piccione R, DeFelipe J, Pastor L. A Method for the Symbolic Representation of Neurons. Front Neuroanat 2018; 12:106. [PMID: 30618651 PMCID: PMC6305400 DOI: 10.3389/fnana.2018.00106] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/20/2018] [Indexed: 11/13/2022] Open
Abstract
The field of neuroanatomy has progressed considerably in recent decades, thanks to the emergence of novel methods which provide new insights into the organization of the nervous system. These new methods have produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data. In other disciplines, such as in many engineering areas, scientists and engineers are dealing with increasingly complex systems, using hierarchical decompositions, graphical models and simplified schematic diagrams for analysis and design processes. This approach makes it possible for users to simultaneously combine global system views and very detailed representations of specific areas of interest, by selecting appropriate representations for each of these views. In this way, users can concentrate on specific details while also maintaining a general system overview - a capability that is essential for understanding structure and function whenever complexity is an issue. Following this approach, this paper focuses on a graphical tool designed to help neuroanatomists to better understand and detect morphological characteristics of neuronal cells. The method presented here, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has proven to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies. A software tool has been developed to generate graphical representations of neurons from 3D computer-aided reconstruction files.
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Affiliation(s)
- Jose Juan Aliaga Maraver
- Departamento de Aeronaves y Vehículos Espaciales, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Susana Mata
- Department of Computer Engineering, Universidad Rey Juan Carlos, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ruth Benavides-Piccione
- Cajal Institute (CSIC), Madrid, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier DeFelipe
- Cajal Institute (CSIC), Madrid, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Luis Pastor
- Department of Computer Engineering, Universidad Rey Juan Carlos, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
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Senk J, Carde C, Hagen E, Kuhlen TW, Diesmann M, Weyers B. VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output. Front Neuroinform 2018; 12:75. [PMID: 30467469 PMCID: PMC6236002 DOI: 10.3389/fninf.2018.00075] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 10/10/2018] [Indexed: 11/13/2022] Open
Abstract
Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity, and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. In order to cover the surface area captured by today's experimental techniques and to achieve sufficient self-consistency, such models contain millions of nerve cells. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based, and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model incorporating distance-dependent connectivity subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Interactive multi-view analysis therefore assists existing data analysis workflows. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis, and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.
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Affiliation(s)
- Johanna Senk
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Corto Carde
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
- JARA - High-Performance Computing, Aachen, Germany
- IMT Atlantique Bretagne-Pays de la Loire, Brest, France
| | - Espen Hagen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, University of Oslo, Oslo, Norway
| | - Torsten W. Kuhlen
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
- JARA - High-Performance Computing, Aachen, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
| | - Benjamin Weyers
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
- JARA - High-Performance Computing, Aachen, Germany
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