Mehdi Khamassi

Cognitive Neuroscience · 2006–2020

A researcher in the Science corpus: 7 papers; trusts 29 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 144 citations into it; #3,761 of 10,852 by trust here, #1,790 by citations
• Cellular and Molecular Neuroscience — 82 citations into it; #1,588 of 2,998 by trust here, #655 by citations
• Artificial Intelligence — 55 citations into it; #27,923 of 35,345 by trust here, #30,347 by citations
• Reinforcement Learning — 54 citations into it
• Neuroscience — 33 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Molecular Biology — 9 citations into it
• Developmental and Educational Psychology — 5 citations into it

Whom Mehdi Khamassi trusts overall

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

1. Richard S. Sutton
2. Kenji Doya
3. Peter S. Dayan
4. Wolfram Schultz
5. Andrew Barto
6. J. Krichmar
7. Nathaniel D. Daw
8. György Buzsáki
9. Angelo Arleo
10. Ben J. A. Kröse

Who trusts Mehdi Khamassi overall

1. Romain D. Cazé
2. Vieri Giuliano Santucci
3. Gianluca Baldassarre
4. Tyler L. Hayes
5. Alex Roxin
6. Annabelle C. Singer
7. David Tingley
8. Dhakshin Ramanathan
9. Jane X. Wang
10. Lycia D. de Voogd

Papers from Mehdi Khamassi's perspective

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

1. Reinforcement Learning: An Introduction
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
6. Long Short-Term Memory
7. Learning internal representations by error propagation
8. Reinforcement Learning: A Survey
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. A Neural Substrate of Prediction and Reward

Their papers

• DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics (2020) — 19 citations
• Combining Self-organizing Maps with Mixtures of Experts: Application to an Actor-Critic Model of Reinforcement Learning in the Basal Ganglia (2006) — 24 citations
• Hippocampal replays under the scrutiny of reinforcement learning models. (2018) — 42 citations
• Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. (2013) — 67 citations
• Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model (2011) — 93 citations
• Coherent theta oscillations and reorganization of spike timing in the hippocampal- prefrontal network upon learning. (2010) — 868 citations
• Replay of rule-learning related neural patterns in the prefrontal cortex during sleep (2009) — 782 citations