Johanni Brea

Cognitive Neuroscience · 2011–2018

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 64 citations into it; #4,844 of 10,852 by trust here, #5,844 by citations
• Cellular and Molecular Neuroscience — 54 citations into it
• Electrical and Electronic Engineering — 43 citations into it; #6,646 of 16,539 by trust here, #12,348 by citations
• Artificial Intelligence — 27 citations into it; #19,786 of 35,345 by trust here, #25,809 by citations
• Reinforcement Learning — 16 citations into it
• Model Architectures — 9 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Johanni Brea trusts overall

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

1. Geoffrey E. Hinton
2. John J. Hopfield
3. David H. Ackley
4. Richard S. Sutton
5. Ilya Sutskever
6. David A. Elizondo
7. Wulfram Gerstner
8. Graham W. Taylor
9. Mantas Lukosevicius
10. Andrew Barto

Who trusts Johanni Brea overall

1. Danilo Jimenez Rezende
2. J. Urgen Schmidhuber
3. Wulfram Gerstner
4. Bernhard Nessler
5. Nicolas Frémaux
6. Friedemann Zenke
7. Walter Senn
8. Michael Pfeiffer
9. Yash Goyal
10. Ronald L. Davis

Papers from Johanni Brea's perspective

Ordered by how strongly the researchers Johanni Brea 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Matching Recall and Storage in Sequence Learning with Spiking Neural Networks (2013) — 118 citations
• Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018) — 347 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