Nicolas Antille's research while affiliated with École Polytechnique Fédérale de Lausanne and other places
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Publications (8)
Motivation:
From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, a...
Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions.
Background
We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discusse...
Background We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discusse...
Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, whi...
We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm...
We have established a multi-constraint, data-driven process to digitally reconstruct, and simulate prototypical neocortical microcircuitry, using sparse experimental data. We applied this process to reconstruct the microcircuitry of the somatosensory cortex in juvenile rat at the cellular and synaptic levels. The resulting reconstruction is broadly...
Citations
... Thin gray represent the 20 individual repetitions, while the thicker black ones their means. Renderings of morphologies (on B as well) were done with NeuroMorphoVis (Abdellah et al., 2018). Neurite diameters are scaled (x3) for better resolution. ...
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... The first is concerned with creating mesh models that are convenient for visual analytics. Depending on the objective, these meshes could be highly tessellated if being used for single cell visualization and analysis, or low tessellated when a visualization of full-compartmental simulation of large scale circuit is essential (Eilemann et al., 2012(Eilemann et al., , 2017Markram et al., 2015). Although they might have various tessellation levels, these meshes are accurate enough to capture all the details of a given morphology, however, they are not required to be watertight (Brito et al., 2013;Garcia-Cantero et al., 2017;Lasserre et al., 2012). ...
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... Neuromorphovis (Abdellah et al., 2018) extends an earlier meshing algorithm (Abdellah et al., 2017) and is capable of reconstructing piecewise watertight meshes that could be employed to visualize detailed electrophysiological activities obtained from voltage dynamics simulations. However, too many metaobjects have to be placed for creating a smoothvarying implicit field for extracting a neuronal membrane iso-surface. ...
... It is even more challenging to optimize such models for in vivo data, as it needs iterative simulations of the models. The DeepDendrite framework can directly support many state-of-the-art large-scale circuit models [86][87][88] , which were initially developed based on NEURON. Moreover, using our framework, a single GPU card such as Tesla A100 could easily support the operation of detailed circuit models of up to 10,000 neurons, thereby providing carbon-efficient and affordable plans for ordinary labs to develop and optimize their own large-scale detailed models. ...
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... Briefly, we adapted a set of multi-compartmental, conductance-based cortical neuron models from the library of models released by the Blue Brain Project [40,41] in NEURON v7.7 [34]. These models included 3D, reconstructed dendritic and axonal morphologies from all 6 cortical layers with up to 13 different published, Hodgkin-Huxley-like ion channel models distributed in the soma, dendrites, and axon. ...
