The state of MIIND

Kamps, Marc de; Baier, Volker; Drever, Johannes; Dietz, Melanie; Mösenlechner, Lorenz; Velde, Frank van der

doi:10.1016/j.neunet.2008.07.006

Cited by 25 publications

(17 citation statements)

References 59 publications

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“…In [3] a generic method was presented for solving this equation efficiently, both for small synaptic efficacies (diffusion limit; the PDE becomes a Fokker-Planck equation) and for large ones (finite jumps). We demonstrated that for leaky-integrate-and-fire (LIF) neurons this method reproduces analytic results [1] and uses of the order of 0.2 s to model 1s simulation time of infinitely large population of spiking LIF neurons.…”

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confidence: 91%

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pMIIND-an MPI-based population density simulation framework

Kamps

Sichau

2013

BMC Neurosci

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confidence: 91%

“…MIIND [1] is the first publicly available implementation of population density algorithms. Like neural mass models, they model at the population level, rather than that of individual neurons, but unlike neural mass models, they consider the full neuronal state space.…”

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confidence: 99%

pMIIND-an MPI-based population density simulation framework

Kamps

Sichau

2013

BMC Neurosci

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“…This was made possible by progress in computer hardware as well as in simulation technology for models ranging from the molecular dynamics of ion channels via detailed compartmental models of individual nerve cells (neurons) to brain-scale networks of simple neuron models and field models. Today, simulation codes exist for all of these levels, but the degree of usage by the community varies (Carnevale and Hines, 2006; de Kamps et al, 2008; Helias et al, 2012; Hepburn et al, 2012; Ritter et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

Constructing Neuronal Network Models in Massively Parallel Environments

Ippen

Eppler

Pleßer

et al. 2017

Front. Neuroinform.

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Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

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“…Various specialized tools have developed since then (O'Reilly, 2014), such as PDP ++ (McClelland and Rumelhart, 1989; O'Reilly et al, 2000), the Neural Simulation Language (Weitzenfeld et al, 2002), emergent (O'Reilly et al, 2012), the simulation platform DANA (Rougier and Fix, 2012), TheVirtualBrain (Sanz Leon et al, 2013), Topographica (Bednar, 2009) and the Neural Field Simulator (Nichols and Hutt, 2015). Similarly, efficient simulators for population-density approaches ( MIIND : de Kamps et al, 2008, DiPDE : Cain et al, 2016) as well as spiking neural networks (see Brette et al, 2007 for a review) have evolved. The foci of the latter range from detailed neuron morphology ( NEURON : Carnevale and Hines, 2006, GENESIS : Bower and Beeman, 2007) to an abstraction of neurons without spatial extent ( NEST: Bos et al, 2015, BRIAN : Goodman and Brette, 2013).…”

Section: Introductionmentioning

confidence: 99%

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

Hahne

Dahmen

Schuecker

et al. 2017

Front. Neuroinform.

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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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The state of MIIND

Cited by 25 publications

References 59 publications

pMIIND-an MPI-based population density simulation framework

pMIIND-an MPI-based population density simulation framework

Constructing Neuronal Network Models in Massively Parallel Environments

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

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