Mattia Rigotti
Cognitive Neuroscience · 2013–2020
A researcher in the Science corpus: 6 papers; trusts 22 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 86 citations into it; #1,803 of 10,852 by trust here, #927 by citations
- Cellular and Molecular Neuroscience — 29 citations into it
- Artificial Intelligence — 28 citations into it; #9,924 of 35,345 by trust here, #5,084 by citations
- Electrical and Electronic Engineering — 18 citations into it; #2,301 of 16,539 by trust here, #3,037 by citations
- Neuroscience — 18 citations into it; #405 of 487 by trust here, #109 by citations
- Representation & Self-Supervised Learning — 11 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
Whom Mattia Rigotti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nathaniel D. Daw
- David Sussillo
- Kenji Doya
- Geoffrey E. Hinton
- R. Fisher
- Herbert Jaeger
- Vladimir Naumovich Vapni
- L. F. Abbott
- Emilio Salinas
- Omri Barak
Who trusts Mattia Rigotti overall
- Timo Flesch
- Rafael Yuste
- John P. Cunningham
- Stefano Fusi
- S. Bernardi
- N. A. Cayco-Gajic
- Marcus K. Benna
- Keno Juechems
- Srdjan Ostojic
- Andrew M. Saxe
Papers from Mattia Rigotti's perspective
Ordered by how strongly the researchers Mattia Rigotti trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
Their papers
- The importance of mixed selectivity in complex cognitive tasks (2013) — 1,630 citations
- The Sparseness of Mixed Selectivity Neurons Controls the Generalization-Discrimination Trade-Off (2013) — 230 citations
- Why neurons mix: high dimensionality for higher cognition. (2016) — 750 citations
- The geometry of abstraction in hippocampus and prefrontal cortex (2020) — 432 citations
- Abstract context representations in primate amygdala and prefrontal cortex (2015) — 170 citations
- Hippocampal-prefrontal input supports spatial encoding in working memory (2015) — 647 citations