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Computer Science

D-Index
57
Citations
13567
World Ranking
3821
National Ranking
171

Overview

Markus Diesmann is affiliated with RWTH Aachen University in Germany, where their research primarily focuses on various aspects of neuroscience. Their body of work spans multiple subfields, including Cognitive Neuroscience, Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Artificial Intelligence, and General Health Professions.

The main topics covered by Diesmann's research include:

  • Neural dynamics and brain function
  • Advanced Memory and Neural Computing
  • Neuroscience and Neural Engineering
  • Functional Brain Connectivity Studies
  • EEG and Brain-Computer Interfaces
  • Neural Networks and Applications
  • Photoreceptor and optogenetics research

Diesmann has frequently published in notable research venues, with several contributions to:

  • arXiv (Cornell University)
  • Frontiers in Neuroinformatics
  • PLoS Computational Biology
  • Zenodo (CERN European Organization for Nuclear Research)
  • bioRxiv (Cold Spring Harbor Laboratory)

Among recent papers authored under their collaboration, notable works include:

  • Connectivity concepts in neuronal network modeling, 2022, PLoS Computational Biology
  • Bringing Anatomical Information into Neuronal Network Models, 2021, Advances in Experimental Medicine and Biology
  • The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing, 2024, Imaging Neuroscience
  • Global organization of neuronal activity only requires unstructured local connectivity, 2021, eLife
  • Conditions for wave trains in spiking neural networks, 2020, Physical Review Research

Frequent co-authors who have collaborated with Diesmann include:

  • Sacha J. van Albada
  • Johanna Senk
  • Tom Tetzlaff
  • Claus C. Hilgetag
  • Hans Ekkehard Pleßer

Best Publications

  • NEST (NEural Simulation Tool)

    Marc-Oliver Gewaltig;Markus Diesmann

  • Stable propagation of synchronous spiking in cortical neural networks

    Markus Diesmann;Marc-Oliver Gewaltig;Marc-Oliver Gewaltig;Ad Aertsen

  • Simulation of networks of spiking neurons: A review of tools and strategies

    Romain Brette;Michelle Rudolph;Ted Carnevale;Michael L. Hines

  • Phenomenological models of synaptic plasticity based on spike timing

    Abigail Morrison;Markus Diesmann;Wulfram Gerstner

  • The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model

    Tobias C. Potjans;Markus Diesmann

  • Spike-Timing-Dependent Plasticity in Balanced Random Networks

    Abigail Morrison;Ad Aertsen;Markus Diesmann

  • Unitary events in multiple single-neuron spiking activity: I. detection and significance

    Sonja Grün;Markus Diesmann;Ad Aertsen

  • PyNEST: A Convenient Interface to the NEST Simulator.

    Jochen Martin Eppler;Moritz Helias;Eilif Muller;Markus Diesmann

  • Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing

    Abigail Morrison;Carsten Mehring;Theo Geisel;Theo Geisel Ad Aertsen

  • Decorrelation of neural-network activity by inhibitory feedback.

    Tom Tetzlaff;Moritz Helias;Gaute T. Einevoll;Markus Diesmann;Markus Diesmann

  • Exact digital simulation of time-invariant linear systems with applications to neuronal modeling.

    Stefan Rotter;Markus Diesmann

  • Unitary events in multiple single-neuron spiking activity: II. nonstationary data

    Sonja Grün;Markus Diesmann;Ad Aertsen

  • Activity dynamics and propagation of synchronous spiking in locally connected random networks.

    Carsten Mehring;Ulrich Hehl;Masayoshi Kubo;Markus Diesmann

  • The Scientific Case for Brain Simulations

    Gaute T. Einevoll;Gaute T. Einevoll;Alain Destexhe;Alain Destexhe;Markus Diesmann;Sonja Grün

  • CoCoMac 2.0 and the future of tract-tracing databases.

    Rembrandt Bakker;Rembrandt Bakker;Thomas Wachtler;Markus Diesmann;Markus Diesmann

  • A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems

    Daniel Brüderle;Mihai A. Petrovici;Bernhard Vogginger;Matthias Ehrlich

  • Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

    Sacha J. van Albada;Andrew G. Rowley;Johanna Senk;Michael Hopkins

  • MEA-Tools: an open source toolbox for the analysis of multi-electrode data with MATLAB.

    U. Egert;Th. Knott;C. Schwarz;M. Nawrot

  • Propagation of synchronous spiking activity in feedforward neural networks

    A Aertsen;M Diesmann;MO Gewaltig

  • Reconstructing neuronal circuitry from parallel spike trains.

    Ryota Kobayashi;Ryota Kobayashi;Shuhei Kurita;Anno Kurth;Katsunori Kitano

  • A general and efficient method for incorporating precise spike times in globally time-driven simulations.

    Alexander Hanuschkin;Susanne Kunkel;Moritz Helias;Abigail Morrison

Frequent Co-Authors

Gaute T. Einevoll
Gaute T. Einevoll Norwegian University of Life Sciences
Alexa Riehle
Alexa Riehle Aix-Marseille University
Ad Aertsen
Ad Aertsen University of Freiburg
Tomoki Fukai
Tomoki Fukai Okinawa Institute of Science and Technology
Alain Destexhe
Alain Destexhe Centre national de la recherche scientifique, CNRS
John Rinzel
John Rinzel New York University
Theo Geisel
Theo Geisel Max Planck Institute for Dynamics and Self-Organization
Henry Markram
Henry Markram École Polytechnique Fédérale de Lausanne
Peter De Weerd
Peter De Weerd Maastricht University
Michael L. Hines
Michael L. Hines Yale University

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