Walter Senn

Cognitive Neuroscience · 2001–2019

A researcher in the Science corpus: 15 papers; trusts 70 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 142 citations into it; #511 of 10,852 by trust here, #877 by citations
• Cellular and Molecular Neuroscience — 93 citations into it; #319 of 2,998 by trust here, #693 by citations
• Electrical and Electronic Engineering — 77 citations into it; #431 of 16,539 by trust here, #2,883 by citations
• Neuroscience — 49 citations into it
• Artificial Intelligence — 46 citations into it; #9,177 of 35,345 by trust here, #13,163 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Reinforcement Learning — 25 citations into it
• Model Architectures — 21 citations into it

Whom Walter Senn trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Stefano Fusi
4. John A. Hertz
5. Erkki Oja
6. John J. Hopfield
7. Guoqiang Bi
8. Wulfram Gerstner
9. Yoshua Bengio
10. Henry Markram

Who trusts Walter Senn overall

1. Wulfram Gerstner
2. Stefano Fusi
3. Andrea Soltoggio
4. Friedemann Zenke
5. Nicolas Frémaux
6. Elisabetta Chicca
7. Pierre Yger
8. Claudia Clopath
9. Giacomo Indiveri
10. Danilo Jimenez Rezende

Papers from Walter Senn's perspective

Ordered by how strongly the researchers Walter Senn trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Matching Recall and Storage in Sequence Learning with Spiking Neural Networks (2013) — 118 citations
• Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting (2019) — 12 citations
• An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing (2001) — 255 citations
• Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics (2007) — 367 citations
• Reinforcement learning in populations of spiking neurons (2008) — 110 citations
• Learning Only When Necessary: Better Memories of Correlated Patterns in Networks with Bounded Synapses (2005) — 49 citations
• Spatio-Temporal Credit Assignment in Neuronal Population Learning (2011) — 66 citations
• A Synaptic Explanation of Suppression in Visual Cortex (2002) — 241 citations
• Dendritic cortical microcircuits approximate the backpropagation algorithm (2018) — 373 citations
• A deep learning framework for neuroscience (2019) — 1,016 citations
• A Normative Theory of Forgetting: Lessons from the Fruit Fly (2014) — 32 citations
• Sequence learning with hidden units in spiking neural networks (2011) — 45 citations
• Learning Spike-Based Population Codes by Reward and Population Feedback (2010) — 16 citations
• Dendritic encoding of sensory stimuli controlled by deep cortical interneurons (2009) — 380 citations
• Eluding oblivion with smart stochastic selection of synaptic updates. (2006) — 24 citations