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.
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.
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.
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.
Spiking Neuron Models: Single Neurons, Populations, Plasticity
Wulfram Gerstner;Werner M. Kistler.
Spiking Neuron Models
W. Gerstner;W. K. Kistler.
Reference Module in Neuroscience and Biobehavioral Psychology#R##N#Encyclopedia of Neuroscience (2002)
A neuronal learning rule for sub-millisecond temporal coding
Wulfram Gerstner;Wulfram Gerstner;Richard Kempter;J. Leo van Hemmen;Hermann Wagner;Hermann Wagner.
Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
Romain Brette;Wulfram Gerstner.
Journal of Neurophysiology (2005)
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)
Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
Wulfram Gerstner;Werner M. Kistler;Richard Naud;Liam Paninski.
Spiking Neuron Models: An Introduction
Wulfram Gerstner;Werner Kistler.
Hebbian learning and spiking neurons
Richard Kempter;Wulfram Gerstner;J. Leo van Hemmen.
Physical Review E (1999)
Time structure of the activity in neural network models
Physical Review E (1995)
Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity
Jean-Pascal Pfister;Wulfram Gerstner.
The Journal of Neuroscience (2006)
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.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: