F. Schürmann

发表

Reconstruction and Simulation of Neocortical Microcircuitry

James G. King, Thomas K. Berger, Michael W. Reimann, 2015, Cell.

Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits

Sean L. Hill, H. Markram, Yun Wang, 2012, Proceedings of the National Academy of Sciences.

The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex

James G. King, Henry Markram, Giuseppe Chindemi, 2015, Front. Neural Circuits.

Intrinsic morphological diversity of thick‐tufted layer 5 pyramidal neurons ensures robust and invariant properties of in silico synaptic connections

James G. King, Sean L. Hill, H. Markram, 2012, The Journal of physiology.

Combinatorial Expression Rules of Ion Channel Genes in Juvenile Rat (Rattus norvegicus) Neocortical Neurons

Henry Markram, Felix Schürmann, Sean L. Hill, 2012, PloS one.

Preserving axosomatic spiking features despite diverse dendritic morphology.

Henry Markram, Idan Segev, Felix Schürmann, 2013, Journal of neurophysiology.

Henry Markram and Idan Segev dendritic morphology Preserving axosomatic spiking features despite diverse

Idan Segev, H. Markram, Etay Hay, 2013 .

SCOUT : Prefetching for Latent Structure Following Queries

H. Markram, A. Ailamaki, T. Heinis, 2012 .

Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling.

J. S. Coggan, H. Markram, D. Keller, 2019, Journal of theoretical biology.

M ODERNIZING THE NEURON S IMULATOR FOR S USTAINABILITY , P ORTABILITY , AND P ERFORMANCE

James G. King, S. Dura-Bernal, M. Hines, 2022 .

Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex

H. Markram, E. Lein, C. D. de Kock, 2022, Cerebral cortex.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

James G. King, S. Dura-Bernal, M. Hines, 2022, bioRxiv.

Speeding Up Range Queries For Brain Simulations

H. Markram, A. Ailamaki, T. Heinis, 2011 .

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

James G. King, Michael W. Reimann, Werner Van Geit, 2022 .

Meshing of Spiny Neuronal Morphologies using Union Operators

Nadir Román Guerrero, M. Abdellah, F. Schürmann, 2022 .

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience

Nadir Román Guerrero, J. S. Coggan, H. Markram, 2022, bioRxiv.

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience

Andrew P. Davison, L. L. Bologna, M. Migliore, 2021, Neuroinformatics.

Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation

H. Markram, M. Abdellah, A. Bilgili, 2017, BMC Bioinformatics.

Editorial Manager(tm) for Biological Cybernetics Manuscript Draft Title: the Quantitative Single-neuron Modeling Competition the Quantitative Single-neuron Modeling Competition

Thomas K. Berger, Arnd, W. Gerstner, 2022 .

NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks

Henry Markram, Felix Schürmann, Marwan Abdellah, 2018, Bioinform..

An efficient analytical reduction of detailed nonlinear neuron models

Noa Rogozinski, Felix Schürmann, Oren Amsalem, 2020, Nature Communications.

Channelpedia: An Integrative and Interactive Database for Ion Channels

Henry Markram, Felix Schürmann, Srikanth Ramaswamy, 2011, Front. Neuroinform..

Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

Henry Markram, Idan Segev, Felix Schürmann, 2011, PLoS Comput. Biol..

A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data

Henry Markram, Idan Segev, Felix Schürmann, 2007, Front. Neurosci..

Cyme: A Library Maximizing SIMD Computation on User-Defined Containers

Timothée Ewart, Felix Schürmann, Fabien Delalondre, 2014, ISC.

A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble

Markus Hadwiger, Corrado Calì, Henry Markram, 2018, Front. Neurosci..

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

Felix Schürmann, Egidio D'Angelo, Stefano Masoli, 2017, Front. Cell. Neurosci..

The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow

Szabolcs Káli, Henry Markram, Felix Schürmann, 2018, PLoS Comput. Biol..

Effective Stimuli for Constructing Reliable Neuron Models

Thomas K. Berger, Henry Markram, Idan Segev, 2011, PLoS Comput. Biol..

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

Henry Markram, Giuseppe Chindemi, Idan Segev, 2016, Front. Neuroinform..

Representing Stimulus Information in an Energy Metabolism Pathway.

J. S. Coggan, H. Markram, D. Keller, 2022, Journal of theoretical biology.

Digital Reconstruction of the Neuro-Glia-Vascular Architecture

H. Markram, M. Abdellah, Michael Gevaert, 2021, Cerebral cortex.

A Derived Positional Mapping of Inhibitory Subtypes in the Somatosensory Cortex

H. Markram, D. Keller, J. Meystre, 2019, Front. Neuroanat..

Exploring liquid computing in a hardware adaptation: construction and operation of a neural network experiment

Felix Schürmann, F. Schürmann, 2005 .

The Scientific Case for Brain Simulations

Sonja Grün, Markus Diesmann, Gaute T. Einevoll, 2019, Neuron.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Silvestro Micera, Johannes Schemmel, Szabolcs Káli, 2017, BMC Neuroscience.

ARC: An Open Web-Platform for Request/Supply Matching for a Prioritized and Controlled COVID-19 Response

Benjamin R. K. Roller, H. Markram, J. Courcol, 2021, Frontiers in Public Health.

A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis.

Henry Markram, Idan Segev, Felix Schürmann, 2013, Cerebral cortex.

A Platform for Parallel Operation of VLSI Neural Networks

A. Grübl, K. Meier, J. Schemmel, 2004 .

Caries Detection with Near-Infrared Transillumination Using Deep Learning

T. H. Newton, H. Markram, T. Newton, 2019, Journal of dental research.

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

P. Kumbhar, F. Schürmann, A. Donaldson, 2023, CC.

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

James G. King, E. De Schutter, P. Kumbhar, 2022, bioRxiv.

Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks

Michael L. Hines, Felix Schürmann, Thomas Sterling, 2019, ICCS.

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

James G. King, Michael L. Hines, Omar Awile, 2019, ICCS.

Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks

Michael L. Hines, Felix Schürmann, Thomas Sterling, 2019, 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

The quantitative single-neuron modeling competition

Wulfram Gerstner, Thomas K. Berger, Richard Naud, 2008, Biological Cybernetics.

Identifying, tabulating, and analyzing contacts between branched neuron morphologies

Henry Markram, Felix Schürmann, James Kozloski, 2008, IBM J. Res. Dev..

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

James G. King, Felix Schürmann, Fabien Delalondre, 2019, Front. Neuroinform..

Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer

Michael L. Hines, Sameer Kumar, Felix Schürmann, 2011, Front. Comput. Neurosci..

Fully implicit parallel simulation of single neurons

Michael L. Hines, Henry Markram, Felix Schürmann, 2008, Journal of Computational Neuroscience.

Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models

F. Schürmann, Francesco Cremonesi, 2020, Neuroinformatics.

Analytic performance modeling and analysis of detailed neuron simulations

Gerhard Wellein, Georg Hager, Francesco Cremonesi, 2019, Int. J. High Perform. Comput. Appl..

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON

Michael L. Hines, William W. Lytton, Felix Schürmann, 2016, Neural Computation.

Exploring The Parameter Space Of A Genetic Algorithm For Training An Analog Neural Network

Johannes Schemmel, Karlheinz Meier, Felix Schürmann, 2002, GECCO.

A Neuron Membrane Mesh Representation for Visualization of Electrophysiological Simulations

Henry Markram, Felix Schürmann, Sebastien Lasserre, 2012, IEEE Transactions on Visualization and Computer Graphics.

Architecture of the Neuro-Glia-Vascular System

H. Markram, M. Abdellah, Michael Gevaert, 2021, bioRxiv.

Metaball skinning of synthetic astroglial morphologies into realistic mesh models for in silico simulations and visual analytics

Nadir Román Guerrero, J. S. Coggan, H. Markram, 2021, Bioinform..

Practical Parallel Rendering of Detailed Neuron Simulations

Felix Schürmann, John Biddiscombe, Stefan Eilemann, 2013, EGPGV@Eurographics.

Parallel Rendering on Hybrid Multi-GPU Clusters

Renato Pajarola, Felix Schürmann, Marwan Abdellah, 2012, EGPGV@Eurographics.

Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate

Henry Markram, Heikki Lehväslaiho, Felix Schürmann, 2018, PLoS computational biology.

Speeding up Hardware Evolution: A Coprocessor for Evolutionary Algorithms

Johannes Schemmel, Karlheinz Meier, Felix Schürmann, 2003, ICES.

Accelerating Range Queries for Brain Simulations

Thomas Heinis, Anastasia Ailamaki, Henry Markram, 2012, 2012 IEEE 28th International Conference on Data Engineering.

Asynchronous Branch-Parallel Simulation of Detailed Neuron Models

Thomas Sterling, Felix Schürmann, Bruno R. C. Magalhães, 2019, Front. Neuroinform..

Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks

Michael L. Hines, Felix Schürmann, Thomas Sterling, 2019, ICCS.

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

Michael W. Reimann, Werner Van Geit, Nadir Román Guerrero, 2023, bioRxiv.

An Approach to Capturing Neuron Morphological Diversity

Sean L. Hill, H. Markram, Imad Riachi, 2009 .

Reconstruction of the Hippocampus.

H. Markram, M. Migliore, F. Schürmann, 2022, Advances in Experimental Medicine and Biology.

An efficient analytical reduction of detailed nonlinear neuron models

I. Segev, Michael Gevaert, P. Kumbhar, 2020, Nature Communications.

Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors

Michael L. Hines, Felix Schürmann, Hubert Eichner, 2008, Journal of Computational Neuroscience.

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Michael W. Reimann, Jorge Blanco Alonso, James B. Isbister, 2023, bioRxiv.

Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

Thomas K. Berger, Henry Markram, Idan Segev, 2008, Biological Cybernetics.

Polynomial Evaluation on Superscalar Architecture, Applied to the Elementary Function ex

Timothée Ewart, Francesco Cremonesi, Felix Schürmann, 2020, ACM Trans. Math. Softw..

Physically-based in silico light sheet microscopy for visualizing fluorescent brain models

Henry Markram, Felix Schürmann, Marwan Abdellah, 2015, BMC Bioinformatics.

An Exclusion Zone for Ca2+ Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS Synapse

Henry Markram, Felix Schürmann, Daniel Keller, 2015, PLoS Comput. Biol..

Preserving Axo-somatic Spiking Features Despite Diverse Dendritic 2 Morphology 3 4

Idan Segev, H. Markram, Etay Hay, 2013 .

Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

H. Markram, M. Abdellah, N. Antille, 2017, bioRxiv.

ERROR ANALYSIS AND QUANTIFICATION IN NEURON SIMULATIONS

Michael L. Hines, Stuart Yates, Francesco Cremonesi, 2016 .

High Fidelity Visualization of Large Scale Digitally Reconstructed Brain Circuitry with Signed Distance Functions

Felix Schürmann, Marwan Abdellah, Jonas Karlsson, 2019, 2019 IEEE Visualization Conference (VIS).

Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers

Felix Schürmann, Marwan Abdellah, Cyrille Favreau, 2019, CGVC.

Towards real-time visualization of detailed neural tissue models: View frustum culling for parallel rendering

Felix Schürmann, Luis Pastor, Juan Hernando, 2012, 2012 IEEE Symposium on Biological Data Visualization (BioVis).

A Computational Model of Light-Sheet Fluorescence Microscopy using Physically-based Rendering

Henry Markram, Felix Schürmann, Marwan Abdellah, 2015, Eurographics.

Efficient Distributed Transposition Of Large-Scale Multigraphs And High-Cardinality Sparse Matrices pdf

Felix Schürmann, Bruno R. C. Magalhães, Bruno R. C. Magalhães, 2020, ArXiv.

Report on Comparison of Existing Software Structures and Data Models in Single Neuron Databasing

F. Schürmann, Asif Jan, 2006 .

A Physically Plausible Model for Rendering Highly Scattering Fluorescent Participating Media pdf

Henry Markram, Felix Schürmann, Marwan Abdellah, 2017, ArXiv.

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy

Michael W. Reimann, James B. Isbister, H. Markram, 2023, bioRxiv.

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Michael W. Reimann, Jorge Blanco Alonso, Christian A. Rössert, 2024, bioRxiv.

Key/Value-Enabled Flash Memory for Complex Scientific Workflows with On-Line Analysis and Visualization

Constantine Bekas, Alessandro Curioni, Bernard Metzler, 2016, 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Rebasing I/O for Scientific Computing: Leveraging Storage Class Memory in an IBM BlueGene/Q Supercomputer

Alessandro Curioni, Bernard Metzler, Lars Schneidenbach, 2014, ISC.

SCOUT: Prefetching for Latent Feature Following Queries

H. Markram, A. Ailamaki, T. Heinis, 2012, Proc. VLDB Endow..

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

James C. Knight, M. Diesmann, T. Nowotny, 2023, Frontiers in Neuroinformatics.

The Role of Hub Neurons in Modulating Cortical Dynamics

H. Markram, M. London, I. Segev, 2021, bioRxiv.

Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis

Felix Schürmann, Marwan Abdellah, Daniel Keller, 2020, Bioinform..

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

Michael W. Reimann, Werner Van Geit, Nadir Román Guerrero, 2024, bioRxiv.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Vito Paolo Pastore, Alfred B. Yu, Kelvin S. Oie, 2017, BMC Neuroscience.