| 1. |
- Brette, Romain, et al.
(author)
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Simulation of networks of spiking neurons : A review of tools and strategies
- 2007
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In: Journal of Computational Neuroscience. - : Springer Science and Business Media LLC. - 0929-5313 .- 1573-6873. ; 23:3, s. 349-398
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Research review (peer-reviewed)abstract
- We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.
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| 2. |
- Bruederle, Daniel, et al.
(author)
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A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems
- 2011
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In: Biological Cybernetics. - : Springer Science and Business Media LLC. - 0340-1200 .- 1432-0770. ; 104:4-5, s. 263-296
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Journal article (peer-reviewed)abstract
- In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.
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| 3. |
- Djurfeldt, Mikael, 1967-, et al.
(author)
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Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
- 2010
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In: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 8:1, s. 43-60
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Journal article (peer-reviewed)abstract
- MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. We provide experiences from the adaptation of two neuronal network simulators of different kinds, NEST and MOOSE, to this API. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. We conclude that MUSIC fulfills the design goals of being portable and simple to adapt to existing simulators. In addition, since the MUSIC API enforces independence between the applications, the multi-simulationcould be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.
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| 4. |
- Hagen, Espen, et al.
(author)
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Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks
- 2016
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In: Cerebral Cortex. - : OXFORD UNIV PRESS INC. - 1047-3211 .- 1460-2199. ; 26:12, s. 4461-4496
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Journal article (peer-reviewed)abstract
- With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a similar to 1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
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| 5. |
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| 6. |
- Jordan, Jakob, et al.
(author)
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Extremely Scalable Spiking Neuronal Network Simulation Code : From Laptops to Exascale Computers
- 2018
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In: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 12
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Journal article (peer-reviewed)abstract
- State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.
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| 7. |
- Lansner, Anders, 1949-, et al.
(author)
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Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers
- 2012
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In: Computational Systems Neurobiology. - Dordrecht : Springer. - 9789400738577 ; , s. 283-315
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Book chapter (peer-reviewed)abstract
- The number of neurons and synapses in biological brains is very large, on the order of millions and billions respectively even in small animals like insects and mice. By comparison most neuronal network models developed and simulated up to now have been tiny, comprising many orders of magnitude less neurons than their real counterpart, with an even more dramatic difference when it comes to the number of synapses. In this chapter we discuss why and when it may be important to work with large-scale, if not full-scale, neuronal network and brain models and to run simulations on supercomputers. We describe the state-of-the-art in large-scale neural simulation technology and methodology as well as ways to analyze and visualize output from such simulations. Finally we discuss the challenges and future trends in this field.
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| 8. |
- Lindén, Henrik, et al.
(author)
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Modeling the spatial reach of the LFP
- 2011
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In: Neuron. - : Elsevier BV. - 0896-6273 .- 1097-4199. ; 72:5, s. 859-872
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Journal article (peer-reviewed)abstract
- The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.
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| 9. |
- Senk, Johanna, et al.
(author)
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Connectivity concepts in neuronal network modeling
- 2022
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:9, s. e1010086-
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Journal article (peer-reviewed)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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