Nicolas Frémaux

Cognitive Neuroscience · 2010–2016

A researcher in the Science corpus: 3 papers; trusts 67 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 82 citations into it; #2,646 of 10,852 by trust here, #4,616 by citations
• Cellular and Molecular Neuroscience — 65 citations into it; #614 of 2,998 by trust here, #1,553 by citations
• Electrical and Electronic Engineering — 41 citations into it; #3,131 of 16,539 by trust here, #8,176 by citations
• Neuroscience — 17 citations into it
• Artificial Intelligence — 12 citations into it
• Molecular Biology — 10 citations into it
• Reinforcement Learning — 7 citations into it
• Neurology — 6 citations into it

Whom Nicolas Frémaux trusts overall

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

1. Wolfram Schultz
2. Wulfram Gerstner
3. Razvan V. Florian
4. Eugene M. Izhikevich
5. Ronald J. Williams
6. Michael A. Farries
7. J. Urgen Schmidhuber
8. Dimitri P. Bertsekas
9. Peter S. Dayan
10. Teuvo Kohonen

Who trusts Nicolas Frémaux overall

1. Friedemann Zenke
2. Wulfram Gerstner
3. Wolfram Schultz
4. Michael John O'Brien
5. H. Francis Song
6. Surya Ganguli
7. Narayan Srinivasa
8. Thomas Miconi
9. Guangyu Robert Yang
10. Andrea Soltoggio

Papers from Nicolas Frémaux's perspective

Ordered by how strongly the researchers Nicolas Frémaux 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. Reinforcement Learning: An Introduction
5. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016) — 475 citations
• Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity (2010) — 154 citations
• Perceptual learning, roving and the unsupervised bias. (2012) — 33 citations