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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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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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Stapmanns J, Hahne J, Helias M, Bolten M, Diesmann M, Dahmen D. Event-Based Update of Synapses in Voltage-Based Learning Rules. Front Neuroinform 2021; 15:609147. [PMID: 34177505 PMCID: PMC8222618 DOI: 10.3389/fninf.2021.609147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 04/07/2021] [Indexed: 11/13/2022] Open
Abstract
Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. In some learning rules membrane potentials not only influence synaptic weight changes at the time points of spike events but in a continuous manner. In these cases, synapses therefore require information on the full time course of membrane potentials to update their strength which a priori suggests a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks.
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Affiliation(s)
- Jonas Stapmanns
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, Germany
| | - Jan Hahne
- School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Moritz Helias
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, Germany
| | - Matthias Bolten
- School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
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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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Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M, Diesmann M. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator. Front Neuroinform 2017; 11:34. [PMID: 28596730 PMCID: PMC5442232 DOI: 10.3389/fninf.2017.00034] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 05/01/2017] [Indexed: 01/21/2023] Open
Abstract
Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.
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Affiliation(s)
- Jan Hahne
- School of Mathematics and Natural Sciences, Bergische Universität WuppertalWuppertal, Germany
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research CentreJülich, Germany
| | - Jannis Schuecker
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research CentreJülich, Germany
| | - Andreas Frommer
- School of Mathematics and Natural Sciences, Bergische Universität WuppertalWuppertal, Germany
| | - Matthias Bolten
- School of Mathematics and Natural Sciences, Bergische Universität WuppertalWuppertal, Germany
| | - Moritz Helias
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research CentreJülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen UniversityAachen, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research CentreJülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen UniversityAachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen UniversityAachen, Germany
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Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schürmann F, Hines ML. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput 2016; 28:2063-90. [PMID: 27557104 DOI: 10.1162/neco_a_00876] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.
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Affiliation(s)
- William W Lytton
- Departments of Physiology, Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate Medical Center, Brooklyn 11023, New York, and Kings County Hospital Center, Brooklyn 11203, New York, U.S.A.
| | - Alexandra H Seidenstein
- Departments of Physiology, Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate Medical Center, Brooklyn, NY 11023, and Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, U.S.A.
| | - Salvador Dura-Bernal
- Departments of Physiology, Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate Medical Center, Brooklyn, NY 11023, U.S.A.
| | - Robert A McDougal
- Department of Neuroscience, Yale University, New Haven, CT 06520, U.S.A.
| | - Felix Schürmann
- Blue Brain Project, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, 1015 Geneva, Switzerland
| | - Michael L Hines
- Blue Brain Project, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, 1015 Geneva, Switzerland, and Department of Neuroscience, Yale University, New Haven, CT 06520, U.S.A.
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7
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Muller E, Bednar JA, Diesmann M, Gewaltig MO, Hines M, Davison AP. Python in neuroscience. Front Neuroinform 2015; 9:11. [PMID: 25926788 PMCID: PMC4396193 DOI: 10.3389/fninf.2015.00011] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 03/28/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Eilif Muller
- Center for Brain Simulation, Ecole Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - James A Bednar
- Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK
| | - Markus Diesmann
- Jülich Research Center and Jülich Aachen Research Alliance, Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) 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
| | - Marc-Oliver Gewaltig
- Center for Brain Simulation, Ecole Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Michael Hines
- Department of Neurobiology, Yale University New Haven, CT, USA
| | - Andrew P Davison
- Neuroinformatics group Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique Gif sur Yvette, France
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