World's Best Scientists 2026 revealed!

D-Index & Metrics

Neuroscience

D-Index
48
Citations
11937
World Ranking
6116
National Ranking
125

Engineering and Technology

D-Index
46
Citations
11213
World Ranking
5059
National Ranking
40

Overview

Anders Lansner is affiliated with the Royal Institute of Technology in Sweden. Their research spans multiple fields, predominantly Neuroscience, Computer Science, and Engineering. Within these broad areas, Lansner focuses on subfields such as Cognitive Neuroscience, Electrical and Electronic Engineering, and Artificial Intelligence.

Their work covers a range of topics including Advanced Memory and Neural Computing, Neural Networks and Applications, Neural Dynamics and Brain Function, Memory and Neural Mechanisms, Neuroscience and Neural Engineering, Neuroscience and Neuropharmacology Research, and Ferroelectric and Negative Capacitance Devices.

Anders Lansner has contributed to several recent publications. Notable papers include:

  • "An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity" (2020) published in eNeuro
  • "Fast Hebbian plasticity and working memory" (2023) published in Current Opinion in Neurobiology
  • "Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model" (2022) published in eNeuro
  • "Mapping the BCPNN Learning Rule to a Memristor Model" (2021) published in Frontiers in Neuroscience
  • "A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning" (2023) published in IEEE Transactions on Biomedical Circuits and Systems

Their frequent coauthors include Pawel Herman, Naresh Balaji Ravichandran, Florian Fiebig, Ahmed Hemani, and Nikolaos Chrysanthidis.

Anders Lansner publishes regularly in several venues. The most frequent publication platforms include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Frontiers in Neuroscience, SSRN Electronic Journal, and eNeuro.

Best Publications

  • A Bayesian neural network method for adverse drug reaction signal generation

    Andrew Bate;M. Lindquist;I.R. Edwards;S. Olsson

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

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

  • Neurocognitive Architecture of Working Memory

    Johan Eriksson;Edward K. Vogel;Anders B. Lansner;Fredrik Bergstrom

  • Neural networks that co-ordinate locomotion and body orientation in lamprey

    S. Grillner;T. Deliagina;A. El Manira;R.H. Hill

  • Neuronal network generating locomotor behavior in lamprey: circuitry, transmitters, membrane properties, and simulation.

    Sten Grillner;Peter Wallen;Lennart Brodin;Anders Lansner

  • Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

    Alberto Mazzoni;Alberto Mazzoni;Henrik Lindén;Henrik Lindén;Hermann Cuntz;Hermann Cuntz;Hermann Cuntz;Anders Lansner

  • The cortex as a central pattern generator.

    Rafael Yuste;Jason N MacLean;Jeffrey Smith;Anders Lansner

  • Intrinsic function of a neuronal network - a vertebrate central pattern generator.

    Sten Grillner;Örjan Ekeberg;Abdeljabbar El Manira;Anders Lansner

  • Theta and gamma power increases and alpha/beta power decreases with memory load in an attractor network model

    Mikael Lundqvist;Pawel Herman;Anders Lansner

  • A computer based model for realistic simulations of neural networks

    Ö. Ekeberg;P. Wallén;A. Lansner;H. Tråvén

  • Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations.

    Anders Lansner

  • Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network

    Mikael Lundqvist;Albert Compte;Anders Lansner

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

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

  • Computer simulation of the segmental neural network generating locomotion in lamprey by using populations of network interneurons

    Jeanette Hellgren;Jeanette Hellgren;Sten Grillner;Anders Lansner

  • Towards cortex sized artificial neural systems

    Christopher Johansson;Anders Lansner

  • A computer-based model for realistic simulations of neural networks. II. The segmental network generating locomotor rhythmicity in the lamprey.

    P. Wallen;O. Ekeberg;A. Lansner;L. Brodin

  • Large-Scale Modeling – a Tool for Conquering the Complexity of the Brain

    Mikael Djurfeldt;Örjan Ekeberg;Anders Lansner

  • Bayesian neural networks with confidence estimations applied to data mining

    R. Orre;A. Lansner;A. Bate;M. Lindquist

  • A ONE-LAYER FEEDBACK ARTIFICIAL NEURAL NETWORK WITH A BAYESIAN LEARNING RULE

    Anders Lansner;Örjan Ekeberg

  • Simple cellular and network control principles govern complex patterns of motor behavior.

    Alexander Kozlov;Mikael Huss;Anders Lansner;Jeanette Hellgren Kotaleski

Frequent Co-Authors

Sten Grillner
Sten Grillner Karolinska Institute
Peter Wallén
Peter Wallén Karolinska Institute
Jesper Tegnér
Jesper Tegnér King Abdullah University of Science and Technology
Lennart Brodin
Lennart Brodin Karolinska Institute
Markus Diesmann
Markus Diesmann RWTH Aachen University
Alain Destexhe
Alain Destexhe Centre national de la recherche scientifique, CNRS
Abdeljabbar El Manira
Abdeljabbar El Manira Karolinska Institute
Santiago Marco
Santiago Marco University of Barcelona
Karl Magnus Petersson
Karl Magnus Petersson University of Algarve
Javier DeFelipe
Javier DeFelipe Technical University of Madrid

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