Werner Alfons Hilda Van Geit | EPFL Graph Search

Werner is the Group Leader of the Cells team within the Simulation Neuroscience Division in Blue Brain.

The goal of his team is to build the single cell models which are the building blocks of the larger network models used in the Blue Brain Project. The first step when building these cell models consists of reconstructing, analysing and artificially recreating neuron morphologies. Next, a biophysically detailed electrical model for the different cell types is built. For the latter the team uses automated parameter optimisation tools like Blue Brain’s open source BluePyOpt tool, but they are also active in extending and developing other Blue Brain software packages related to single cells.

A universal workflow for creation, validation and generalization of detailed neuronal models

Alexis Arnaudon, Tanguy Pierre Louis Damart, Darshan Mandge, Henry Markram, Srikanth Ramaswamy, Maria Reva, Christian Andreas Rössert, Mustafa Anil Tuncel, Werner Alfons Hilda Van Geit

Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex (SSCx). Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms specific to the cell type. We use an evolutionary algorithm to optimize passive and active ionic parameters to match the electrophysiological features extracted from whole-cell patch-clamp recordings. To shed light on which parameters are constrained by experimental data and which could be degenerate, we perform a parameter sensitivity analysis. We also validate the optimized models against additional experimental stimuli and assess their generalizability on a population of morphologies with the same morphological type. With this workflow, we generate SSCx neuronal models producing the variability of neuronal responses. Due to its versatility, our workflow can be used to build robust biophysical models of any neuronal type.

2022

Modeling and simulation of rat non-barrel somatosensory cortex. Part I: Modeling anatomy

Alexis Arnaudon, Sirio Bolaños Puchet, Benoît Jean-Albert Coste, Jean-Denis Georges Emile Courcol, Thomas Brice Delemontex, Adrien Michel Achille Devresse, Hugo Thabo Dictus, Alexander Dietz, András Ecker, Daniela Egas Santander, Cyrille Pierre Henri Favreau, Gianluca Ficarelli, Michael Emiel Gevaert, Juan Bautista Hernando Vieites, Joni Henrikki Herttuainen, Kathryn Hess Bellwald, James Bryden Isbister, Lida Kanari, Daniel Keller, James Gonzalo King, Pramod Shivaji Kumbhar, Samuel Lieven D. Lapere, Janis Lazovskis, Ran Levi, Huanxiang Lu, Henry Markram, Eilif Benjamin Muller, Nicolas René Jean Ninin, Judit Planas Carbonell, Christoph Pokorny, Srikanth Ramaswamy, Michael Reimann, Juan Luis Riquelme Roman, Armando Romani, Felix Schürmann, Ying Shi, Vishal Sood, Werner Alfons Hilda Van Geit, Liesbeth Maria L Vanherpe, Matthias Wolf

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, hierarchical, interregional structure with a highly specific laminar architecture. The last decade has seen the emergence of extensive new data sets on anatomy and connectivity at the whole brain scale, providing promising new directions for studies of cortical function that take into account the inseparability of whole-brain and microcircuit architectures. Here, we present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, which integrates whole-brain scale data while providing cellular and subcellular specificity. This multiscale integration was achieved by building the morphologically detailed model of cortical circuitry embedded within a volumetric, digital brain atlas. The model consists of 4.2 million morphologically detailed neurons belonging to 60 different morphological types, placed in the nonbarrel subregions of the Paxinos and Watson atlas. They are connected by 13.2 billion synapses determined by axo-dendritic overlap, comprising local connectivity and long-range connectivity defined by topographic mappings between subregions and laminar axonal projection profiles, both parameterized by whole brain data sets. Additionally, we incorporated core- and matrix-type thalamocortical projection systems, associated with sensory and higher-order extrinsic inputs, respectively. An analysis of the modeled synaptic connectivity revealed a highly nonrandom topology with substantial structural differences but also synergy between local and long-range connectivity. Long-range connections featured a more divergent structure with a comparatively small group of neurons serving as hubs to distribute excitation to far away locations. Taken together with analyses at different spatial granularities, these results support the notion that local and interregional connectivity exist on a spectrum of scales, rather than as separate and distinct networks, as is commonly assumed. Finally, we predicted how the emergence of primary sensory cortical maps is constrained by the anatomy of thalamo-cortical projections. A subvolume of the model comprising 211,712 neurons in the front limb, jaw, and dysgranular zone has been made freely and openly available to the community.

2022