D-Index & Metrics Best Publications

D-Index & Metrics 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.

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
Neuroscience D-index 35 Citations 6,060 113 World Ranking 6014 National Ranking 142

Overview

What is he best known for?

The fields of study he is best known for:

  • Neuroscience
  • Artificial intelligence
  • Neuron

Walter Senn focuses on Neuroscience, Inhibitory postsynaptic potential, Soma, Postsynaptic potential and Communication. As part of his studies on Neuroscience, Walter Senn often connects relevant areas like Artificial neural network. His Inhibitory postsynaptic potential research incorporates themes from Stimulation, Hebbian theory, Premovement neuronal activity and Electroencephalography.

His work deals with themes such as Anti-Hebbian learning, Unsupervised learning, Biological neuron model, Learning rule and Reinforcement learning, which intersect with Soma. His Postsynaptic potential research is multidisciplinary, incorporating perspectives in Synaptic plasticity, Synapse, Neurotransmission and Biological neural network. His biological study spans a wide range of topics, including Sequence, Coding, Unary operation and Scale.

His most cited work include:

  • Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics (280 citations)
  • Dendritic encoding of sensory stimuli controlled by deep cortical interneurons (278 citations)
  • Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity (258 citations)

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

Walter Senn mainly focuses on Neuroscience, Artificial intelligence, Synaptic plasticity, Artificial neural network and Reinforcement learning. His studies in Neuroscience integrate themes in fields like Postsynaptic potential and Hebbian theory. His Artificial intelligence research includes themes of Machine learning, Forgetting and Pattern recognition.

His Synaptic plasticity research incorporates elements of Biological neural network, State and Sensory system. His Artificial neural network research is multidisciplinary, incorporating elements of Algorithm, Synapse, Perception and Visual cortex. His Temporal difference learning study in the realm of Reinforcement learning interacts with subjects such as Reinforcement.

He most often published in these fields:

  • Neuroscience (39.23%)
  • Artificial intelligence (34.62%)
  • Synaptic plasticity (20.00%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (34.62%)
  • Synaptic plasticity (20.00%)
  • Neuromorphic engineering (5.38%)

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

The scientist’s investigation covers issues in Artificial intelligence, Synaptic plasticity, Neuromorphic engineering, Backpropagation and Deep learning. Walter Senn has included themes like Pattern recognition and Feed forward in his Artificial intelligence study. His Synaptic plasticity study combines topics in areas such as Parametrization, Synaptic weight and Neuroscience, Odor.

In his work, Artificial neural network is strongly intertwined with Reinforcement learning, which is a subfield of Synaptic weight. Many of his studies involve connections with topics such as Conditioning and Neuroscience. His work focuses on many connections between Backpropagation and other disciplines, such as Biological neural network, that overlap with his field of interest in Contrast, Pyramidal Neuron, Inhibitory postsynaptic potential and Stimulus.

Between 2017 and 2021, his most popular works were:

  • A deep learning framework for neuroscience (199 citations)
  • Dendritic cortical microcircuits approximate the backpropagation algorithm (92 citations)
  • Dendritic error backpropagation in deep cortical microcircuits. (25 citations)

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

  • Artificial intelligence
  • Neuroscience
  • Neuron

His primary scientific interests are in Neuromorphic engineering, Synaptic plasticity, Artificial intelligence, Backpropagation and Spiking neural network. His Synaptic plasticity study combines topics from a wide range of disciplines, such as Synaptic weight, Speech recognition, Forgetting, Sensory system and Reinforcement learning. Walter Senn mostly deals with Deep learning in his studies of Artificial intelligence.

His Deep learning study integrates concerns from other disciplines, such as Artificial neural network and Neuroscience. His research on Neuroscience frequently connects to adjacent areas such as Variety. The various areas that Walter Senn examines in his Backpropagation study include Biological neural network and Excitatory postsynaptic potential.

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

A deep learning framework for neuroscience

Blake A Richards;Timothy P Lillicrap;Philippe Beaudoin;Yoshua Bengio;Yoshua Bengio.
Nature Neuroscience (2019)

438 Citations

Dendritic encoding of sensory stimuli controlled by deep cortical interneurons

Masanori Murayama;Enrique Pérez-Garci;Thomas Nevian;Tobias Bock.
Nature (2009)

375 Citations

Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

Joseph M. Brader;Walter Senn;Stefano Fusi.
Neural Computation (2007)

370 Citations

Neocortical Pyramidal Cells Respond as Integrate-and-Fire Neurons to In Vivo–Like Input Currents

Alexander Rauch;Giancarlo La Camera;Hans-Rudolf Lüscher;Walter Senn.
Journal of Neurophysiology (2003)

319 Citations

Top-down Dendritic Input Increases the Gain of Layer 5 Pyramidal Neurons

Matthew E. Larkum;Walter Senn;Hans-R. Lüscher.
Cerebral Cortex (2004)

319 Citations

Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity

Ila R. Fiete;Walter Senn;Claude Z.H. Wang;Richard H.R. Hahnloser.
Neuron (2010)

292 Citations

An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing

Walter Senn;Henry Markram;Misha Tsodyks.
Neural Computation (2001)

282 Citations

A Synaptic Explanation of Suppression in Visual Cortex

Matteo Carandini;David J Heeger;Walter Senn.
The Journal of Neuroscience (2002)

268 Citations

Modeling of Spontaneous Activity in Developing Spinal Cord Using Activity-Dependent Depression in an Excitatory Network

Joël Tabak;Walter Senn;Michael J. O'Donovan;John Rinzel.
The Journal of Neuroscience (2000)

222 Citations

A cospectral correction model for measurement of turbulent NO2 flux

W.F Eugster;W. Senn.
Boundary-Layer Meteorology (1995)

221 Citations

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