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.
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.
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.
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.
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A deep learning framework for neuroscience
Blake A Richards;Timothy P Lillicrap;Philippe Beaudoin;Yoshua Bengio;Yoshua Bengio.
Nature Neuroscience (2019)
Dendritic encoding of sensory stimuli controlled by deep cortical interneurons
Masanori Murayama;Enrique Pérez-Garci;Thomas Nevian;Tobias Bock.
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics
Joseph M. Brader;Walter Senn;Stefano Fusi.
Neural Computation (2007)
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)
Top-down Dendritic Input Increases the Gain of Layer 5 Pyramidal Neurons
Matthew E. Larkum;Walter Senn;Hans-R. Lüscher.
Cerebral Cortex (2004)
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.
An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing
Walter Senn;Henry Markram;Misha Tsodyks.
Neural Computation (2001)
A Synaptic Explanation of Suppression in Visual Cortex
Matteo Carandini;David J Heeger;Walter Senn.
The Journal of Neuroscience (2002)
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)
A cospectral correction model for measurement of turbulent NO2 flux
W.F Eugster;W. Senn.
Boundary-Layer Meteorology (1995)
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