Johannes Friedrich
Cognitive Neuroscience · 2010–2019
A researcher in the Science corpus: 5 papers; trusts 14 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 37 citations into it; #5,685 of 10,852 by trust here, #7,348 by citations
- Cellular and Molecular Neuroscience — 36 citations into it; #1,445 of 2,998 by trust here, #2,325 by citations
- Electrical and Electronic Engineering — 14 citations into it; #6,464 of 16,539 by trust here, #11,977 by citations
- Reinforcement Learning — 8 citations into it
- Computational Mechanics — 7 citations into it
- Artificial Intelligence — 6 citations into it
- Statistics and Probability — 6 citations into it
- Computational Theory and Mathematics — 5 citations into it
Whom Johannes Friedrich trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- H. Sebastian Seung
- Ronald J. Williams
- Razvan V. Florian
- Jorge Nocedal
- Peter J. Brockwell
- Ila Rani Fiete
- Sen Song
- Joshua T. Vogelstein
- Brendan O'Donoghue
- Claudia Clopath
Who trusts Johannes Friedrich overall
- Michael John O'Brien
- Nicolas Frémaux
- C. Stringer
- Marius Pachitariu
- Wei Fang
- Wulfram Gerstner
- Narayan Srinivasa
- Andrea Soltoggio
- Zhaofei Yu
Papers from Johannes Friedrich's perspective
Ordered by how strongly the researchers Johannes Friedrich trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- Reinforcement Learning: An Introduction
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Spatio-Temporal Credit Assignment in Neuronal Population Learning (2011) — 66 citations
- Fast online deconvolution of calcium imaging data (2016) — 542 citations
- CaImAn an open source tool for scalable calcium imaging data analysis (2019) — 780 citations
- Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data (2018) — 624 citations
- Learning Spike-Based Population Codes by Reward and Population Feedback (2010) — 16 citations