NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

Koene, Randal A.; Tijms, Betty M.; Hees, Peter van; Postma, Frank; Ridder, Alexander de; Ramakers, G.J.A.; Pelt, Jaap van; Ooyen, Arjen van

doi:10.1007/s12021-009-9052-3

Cited by 156 publications

(179 citation statements)

References 77 publications

(63 reference statements)

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“…They estimate the probabilities for each of these events taking into account some of the different factors involved in neuron development, e.g., molecular gradients (Hentschel and van Ooyen 1999), electric field presence (Robert and Sweeney 1997), neuritic tension (Li and Qin 1996), segment length or centrifugal order (Van Pelt et al 2001), neurotrophic particles (Luczak 2006), etc. One of the recent works that implements this kind of model (Koene et al 2009) has simulated complete networks of neurons. The probability functions include complex elements, e.g., the influence of competition between dendrites when deciding if a bifurcation should occur, the distance between dendrites and axons when establishing synaptic connections, etc.…”

Section: Introductionmentioning

confidence: 99%

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

et al. 2011

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Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.

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Section: Introductionmentioning

confidence: 99%

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

et al. 2011

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“…Due to the high computational complexity, existing frameworks (such as NET-MORPH [27]) can simulate networks of up to a few hundred neurons only (Figure 8, left). With the human brain having an estimated 100 billion neurons, a vast scaling up of the simulations is urgent.…”

Section: Neuroinformaticsmentioning

confidence: 99%

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Seinstra

Maassen

Nieuwpoort

et al. 2011

Computer Communications and Networks

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In recent years, the application of high-performance and distributed computing in scientific practice has become increasingly wide-spread. Among the most widely available platforms to scientists are clusters, grids, and cloud systems. Such infrastructures currently are undergoing revolutionary change due to the integration of many-core technologies, providing orders-of-magnitude speed improvements for selected compute kernels. With high-performance and distributed computing systems thus becoming more heterogeneous and hierarchical, programming complexity is vastly increased. Further complexities arise because urgent desire for scalability, and issues including data distribution, software heterogeneity, and ad-hoc hardware availability, commonly force scientists into simultaneous use of multiple platforms (e.g. clusters, grids, and clouds used concurrently). A true computing jungle. In this chapter we explore the possibilities of enabling efficient and transparent use of Jungle Computing Systems in every-day scientific practice. To this end, we discuss the fundamental methodologies required for defining programming models that are tailored to the specific needs of scientific researchers. Importantly, we claim that many of these fundamental methodologies already exist today, as integrated in our Ibis high-performance distributed programming system. We also make a case for the urgent need for easy and efficient Jungle Computing in scientific practice, by exploring a set of state-of-the-art application domains. For one of these domains we present results obtained with Ibis on a real-world Jungle Computing System. The chapter concludes by exploring fundamental research questions to be investigated in the years to come.

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“…need to be figured out. Tools like NEURON, GENE-SIS, neuroConstruct, and NeuGEN can be used for multicompartment simulation and parametrized synthetic circuit generation/simulation/analysis [61], [62], [63], [64], [65], [66], [67]. Data from the KESM can help narrow down on the range of various parameters for these simulations (see [68] for parameter constraining procedures).…”

Section: Graph Theoretical Analysismentioning

confidence: 99%

Knife-edge scanning microscopy for connectomics research

Choe

Mayerich

Kwon

et al. 2011

The 2011 International Joint Conference on Neural Networks

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In this paper, we will review a novel microscopy modality called Knife-Edge Scanning Microscopy (KESM) that we have developed over the past twelve years (since 1999) and discuss its relevance to connectomics and neural networks research. The operational principle of KESM is to simultaneously section and image small animal brains embedded in hard polymer resin so that a near-isotropic, sub-micrometer voxel size of 0.6 µm × 0.7 µm × 1.0 µm can be achieved over ∼1 cm 3 volume of tissue which is enough to hold an entire mouse brain. At this resolution, morphological details such as dendrites, dendritic spines, and axons are visible (for sparse stains like Golgi). KESM has been successfully used to scan whole mouse brains stained in Golgi (neuronal morphology), Nissl (somata), and India ink (vasculature), providing unprecedented insights into the system-level architectural layout of microstructures within the mouse brain. In this paper, we will present whole-brain-scale data sets from KESM and discuss challenges and opportunities posed to connectomics and neural networks research by such detailed yet system-level data.

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NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

Cited by 156 publications

References 77 publications

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Knife-edge scanning microscopy for connectomics research

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