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Sökning: WFRF:(Beeman David)

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1.
  • Richards, Stephen, et al. (författare)
  • The genome of the model beetle and pest Tribolium castaneum.
  • 2008
  • Ingår i: Nature. - 1476-4687. ; 452:7190, s. 949-55
  • Tidskriftsartikel (refereegranskat)abstract
    • Tribolium castaneum is a representative of earth’s most numerous eukaryotic order, a powerful model organism for the study of generalized insect development, and also an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved an ability to interact with a diverse chemical environment as evidenced by large expansions in odorant and gustatory receptors, as well as p450 and other detoxification enzymes. Developmental patterns in Tribolium are more representative of other arthropods than those found in Drosophila, a fact represented in gene content and function. For one, Tribolium has retained more ancestral genes involved in cell-cell communication than Drosophila, and some are expressed in the growth zone crucial for axial elongation in short germ development. Systemic RNAi in T. castaneum appears to use mechanisms distinct from those found in C. elegans, but nevertheless offers similar power for the elucidation of gene function and identification of targets for selective insect control.
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2.
  • Brette, Romain, et al. (författare)
  • Simulation of networks of spiking neurons : A review of tools and strategies
  • 2007
  • Ingår i: Journal of Computational Neuroscience. - : Springer Science and Business Media LLC. - 0929-5313 .- 1573-6873. ; 23:3, s. 349-398
  • Forskningsöversikt (refereegranskat)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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