D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 59 Citations 19,128 184 World Ranking 657 National Ranking 14
Neuroscience D-index 65 Citations 24,931 169 World Ranking 1106 National Ranking 28

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Neuroscience

His primary areas of study are Neuroscience, Artificial intelligence, Hebbian theory, Spike-timing-dependent plasticity and Excitatory postsynaptic potential. His Neuroscience research includes themes of Nonsynaptic plasticity and Threshold model. His Artificial intelligence research integrates issues from Machine learning, Computer vision and Natural language processing.

His Hebbian theory study is focused on Artificial neural network in general. The Spike-timing-dependent plasticity study combines topics in areas such as Data mining, Anti-Hebbian learning, Synapse, Learning rule and Reinforcement learning. His work carried out in the field of Excitatory postsynaptic potential brings together such families of science as Cortex, Optimal control and Pattern recognition.

His most cited work include:

  • Spiking Neuron Models: Single Neurons, Populations, Plasticity (1941 citations)
  • Spiking Neuron Models (1264 citations)
  • A neuronal learning rule for sub-millisecond temporal coding (956 citations)

What are the main themes of his work throughout his whole career to date?

Wulfram Gerstner mainly focuses on Artificial intelligence, Neuroscience, Artificial neural network, Hebbian theory and Biological system. His Artificial intelligence study incorporates themes from Machine learning, Computer vision and Pattern recognition. Wulfram Gerstner studied Neuroscience and Spike-timing-dependent plasticity that intersect with Anti-Hebbian learning.

Many of his studies on Artificial neural network apply to Statistical physics as well. His research in Hebbian theory intersects with topics in Synapse, Receptive field and Biological neuron model. His Biological system research incorporates elements of Exponential integrate-and-fire, Neuron and Nonlinear system.

He most often published in these fields:

  • Artificial intelligence (41.76%)
  • Neuroscience (31.25%)
  • Artificial neural network (16.76%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (41.76%)
  • Neuroscience (31.25%)
  • Artificial neural network (16.76%)

In recent papers he was focusing on the following fields of study:

Wulfram Gerstner spends much of his time researching Artificial intelligence, Neuroscience, Artificial neural network, Reinforcement learning and Hebbian theory. The various areas that Wulfram Gerstner examines in his Artificial intelligence study include Machine learning, Surprise and Nonlinear system. His studies deal with areas such as Feed forward, Biological system, Neuron and Spiking neural network as well as Nonlinear system.

His research integrates issues of Metaplasticity, Synaptic scaling and Bistability in his study of Neuroscience. Wulfram Gerstner has included themes like Speech recognition and Topology in his Artificial neural network study. His Hebbian theory research is multidisciplinary, relying on both Spike-timing-dependent plasticity, Homeostatic plasticity, Anti-Hebbian learning and Neocortex.

Between 2014 and 2021, his most popular works were:

  • Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks (204 citations)
  • Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules. (176 citations)
  • Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions (118 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Neuroscience

Artificial intelligence, Neuroscience, Artificial neural network, Hebbian theory and Synaptic scaling are his primary areas of study. His Artificial intelligence research includes elements of Machine learning, Biological system and Nonlinear system. His Neuroscience research incorporates themes from Cognitive science and Novelty.

The Spiking neural network and Gradient descent research Wulfram Gerstner does as part of his general Artificial neural network study is frequently linked to other disciplines of science, such as Melody, therefore creating a link between diverse domains of science. The concepts of his Hebbian theory study are interwoven with issues in Spike-timing-dependent plasticity and Homeostatic plasticity. Wulfram Gerstner works mostly in the field of Synaptic scaling, limiting it down to topics relating to Nonsynaptic plasticity and, in certain cases, Synaptic fatigue.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Spiking Neuron Models: Single Neurons, Populations, Plasticity

Wulfram Gerstner;Werner M. Kistler.
(2002)

3961 Citations

Spiking Neuron Models

W. Gerstner;W. K. Kistler.
Reference Module in Neuroscience and Biobehavioral Psychology#R##N#Encyclopedia of Neuroscience (2002)

2030 Citations

A neuronal learning rule for sub-millisecond temporal coding

Wulfram Gerstner;Wulfram Gerstner;Richard Kempter;J. Leo van Hemmen;Hermann Wagner;Hermann Wagner.
Nature (1996)

1225 Citations

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

Romain Brette;Wulfram Gerstner.
Journal of Neurophysiology (2005)

1069 Citations

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

Jd.R. Millan;F. Renkens;J. Mourino;W. Gerstner.
IEEE Transactions on Biomedical Engineering (2004)

910 Citations

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

Wulfram Gerstner;Werner M. Kistler;Richard Naud;Liam Paninski.
(2014)

892 Citations

Spiking Neuron Models: An Introduction

Wulfram Gerstner;Werner Kistler.
(2002)

804 Citations

Hebbian learning and spiking neurons

Richard Kempter;Wulfram Gerstner;J. Leo van Hemmen.
Physical Review E (1999)

697 Citations

Time structure of the activity in neural network models

Wulfram Gerstner.
Physical Review E (1995)

579 Citations

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

Jean-Pascal Pfister;Wulfram Gerstner.
The Journal of Neuroscience (2006)

560 Citations

Best Scientists Citing Wulfram Gerstner

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Nikola Kasabov

Auckland University of Technology

Publications: 100

Markus Diesmann

Markus Diesmann

RWTH Aachen University

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Wolfgang Maass

Wolfgang Maass

Graz University of Technology

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Jan Treur

Jan Treur

Vrije Universiteit Amsterdam

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Giacomo Indiveri

Giacomo Indiveri

University of Zurich

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José del R. Millán

José del R. Millán

The University of Texas at Austin

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Peter A. Tass

Peter A. Tass

Stanford University

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Alain Destexhe

Alain Destexhe

Centre national de la recherche scientifique, CNRS

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Walter Senn

Walter Senn

University of Bern

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Romain Brette

Romain Brette

Institut de la Vision

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Nicolas Brunel

Nicolas Brunel

Duke University

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Terrence J. Sejnowski

Terrence J. Sejnowski

Salk Institute for Biological Studies

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Liam Maguire

Liam Maguire

University of Ulster

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Jianfeng Feng

Jianfeng Feng

Fudan University

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Richard Kempter

Richard Kempter

Humboldt-Universität zu Berlin

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Tomoki Fukai

Tomoki Fukai

Okinawa Institute of Science and Technology

Publications: 37

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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