Peter E. Latham
Cognitive Neuroscience · 2001–2021
A researcher in the Science corpus: 13 papers; trusts 24 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 133 citations into it; #1,285 of 10,852 by trust here, #645 by citations
- Artificial Intelligence — 48 citations into it; #10,948 of 35,345 by trust here, #7,112 by citations
- Cellular and Molecular Neuroscience — 36 citations into it; #1,143 of 2,998 by trust here, #1,013 by citations
- Neuroscience — 21 citations into it
- Electrical and Electronic Engineering — 20 citations into it; #4,320 of 16,539 by trust here, #5,398 by citations
- Experimental and Cognitive Psychology — 15 citations into it
- Probabilistic & Bayesian Methods — 11 citations into it
- Molecular Biology — 10 citations into it; #17,365 of 17,773 by trust here, #8,182 by citations
Whom Peter E. Latham trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Xiao-Jing Wang
- Nicolas J.-B. Brunel
- Jerrold E. Marsden
- John J. Hopfield
- David H. Hubel
- Michael N. Shadlen
- Marc O. Ernst
- E. Gardner
- P. Lennie
- Moshe Abeles
Who trusts Peter E. Latham overall
- Alexandre Pouget
- Andreas Knoblauch
- N. A. Cayco-Gajic
- John M. Beggs
- Adam Kohn
- Stephen M. Fleming
- Yoshiyuki Sato
- Odelia Schwartz
- Stefano Panzeri
- Jeffrey M. Beck
Papers from Peter E. Latham's perspective
Ordered by how strongly the researchers Peter E. Latham trusts endorse each paper. Same corpus, a different order for every researcher.
- Neural networks and physical systems with emergent collective computational abilities.
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Language Models are Unsupervised Multitask Learners
- Deep Learning
- Attention is All you Need (Neural Information Processing Systems)
- Recurrent excitation in neocortical circuits
- Prototypical Networks for Few-shot Learning
Their papers
- Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables (2016) — 85 citations
- Computing and Stability in Cortical Networks (2004) — 77 citations
- A deep learning framework for neuroscience (2019) — 1,016 citations
- Efficient computation and cue integration with noisy population codes (2001) — 390 citations
- Probabilistic population codes for Bayesian decision making (2008) — 674 citations
- Information-limiting correlations (2014) — 530 citations
- Synaptic plasticity as Bayesian inference (2021) — 48 citations
- Confidence matching in group decision-making (2017) — 135 citations
- Post-decisional accounts of biases in confidence (2016) — 86 citations
- Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations (2015) — 22 citations
- Probabilistic brains: knowns and unknowns (2013) — 641 citations
- Bayesian inference with probabilistic population codes (2006) — 738 citations
- Neural correlations, population coding and computation (2006) — 1,770 citations