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.
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