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Neuroscience

D-Index
84
Citations
37290
World Ranking
1368
National Ranking
22

Overview

Wulfram Gerstner is affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their research primarily focuses on neuroscience, with a total of 89 publications in this field. Within neuroscience, they have a significant concentration in cognitive neuroscience, electrical and electronic engineering, artificial intelligence, cellular and molecular neuroscience, and statistical and nonlinear physics.

The main topics Gerstner's work covers include neural dynamics and brain function, advanced memory and neural computing, neural networks and applications, memory and neural mechanisms, neuroscience and neuropharmacology research, functional brain connectivity studies, and neuroscience and neural engineering.

Gerstner has contributed to several scientific journals and preprint servers, with frequent publications in:

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

Significant recent papers authored or co-authored by Gerstner include:

  • "Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response," 2021, Neuron
  • "A taxonomy of surprise definitions," 2022, Journal of Mathematical Psychology
  • "Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making," 2021, PLoS Computational Biology
  • "When shared concept cells support associations: Theory of overlapping memory engrams," 2021, PLoS Computational Biology
  • "Curiosity-driven exploration: foundations in neuroscience and computational modeling," 2023, Trends in Neurosciences

They have collaborated frequently with several researchers, including:

  • Alireza Modirshanechi
  • Johanni Brea
  • Guillaume Bellec
  • Carl C.H. Petersen
  • Vahid Esmaeili

Best Publications

  • Spiking Neuron Models: Single Neurons, Populations, Plasticity

    Wulfram Gerstner;Werner M. Kistler

  • Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity

    Romain Brette;Wulfram Gerstner

  • A neuronal learning rule for sub-millisecond temporal coding

    Wulfram Gerstner;Wulfram Gerstner;Richard Kempter;J. Leo van Hemmen;Hermann Wagner;Hermann Wagner

  • Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition

    Wulfram Gerstner;Werner M. Kistler;Richard Naud;Liam Paninski

  • Noninvasive brain-actuated control of a mobile robot by human EEG

    Jd.R. Millan;F. Renkens;J. Mourino;W. Gerstner

  • Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

    Tim Vogels;Henning Sprekeler;Friedemann Zenke;Claudia Clopath;Claudia Clopath

  • Hebbian learning and spiking neurons

    Richard Kempter;Wulfram Gerstner;J. Leo van Hemmen

  • Spiking Neuron Models: An Introduction

    Wulfram Gerstner;Werner Kistler

  • Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity

    Jean-Pascal Pfister;Wulfram Gerstner

  • Connectivity reflects coding: a model of voltage-based STDP with homeostasis

    Claudia Clopath;Lars Holger Büsing;Lars Holger Büsing;Eleni Vasilaki;Eleni Vasilaki;Wulfram Gerstner

  • Phenomenological models of synaptic plasticity based on spike timing

    Abigail Morrison;Markus Diesmann;Wulfram Gerstner

  • Time structure of the activity in neural network models

    Wulfram Gerstner

  • A History of Spike-Timing-Dependent Plasticity

    Henry Markram;Wulfram Gerstner;Per Jesper Sjöström;Per Jesper Sjöström

  • Mathematical formulations of Hebbian learning.

    Wulfram Gerstner;Werner M. Kistler

  • Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking

    Wulfram Gerstner

  • Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules.

    Wulfram Gerstner;Marco Lehmann;Vasiliki Liakoni;Dane Corneil

  • Spiking Neuron Models

    Unknown

  • Reduction of the hodgkin-huxley equations to a single-variable threshold model

    Werner M. Kistler;Wulfram Gerstner;J. Leo van Hemmen

  • Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules.

    Nicolas Frémaux;Wulfram Gerstner

  • Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

    Friedemann Zenke;Everton J. Agnes;Everton J. Agnes;Wulfram Gerstner

  • Spiking neurons

    Wulfram Gerstner

  • Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity.

    Angelo Arleo;Wulfram Gerstner

Frequent Co-Authors

Claudia Clopath
Claudia Clopath Imperial College London
Michael H. Herzog
Michael H. Herzog École Polytechnique Fédérale de Lausanne
Richard Kempter
Richard Kempter Humboldt-Universität zu Berlin
Carl C. H. Petersen
Carl C. H. Petersen École Polytechnique Fédérale de Lausanne
Henry Markram
Henry Markram École Polytechnique Fédérale de Lausanne
Heidi I.L. Jacobs
Heidi I.L. Jacobs Harvard University
Gaute T. Einevoll
Gaute T. Einevoll Norwegian University of Life Sciences
Hans-Rudolf Lüscher
Hans-Rudolf Lüscher University of Bern
Alain Destexhe
Alain Destexhe Centre national de la recherche scientifique, CNRS

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