Dmitri "Mitya" Chklovskii
Cellular and Molecular Neuroscience · 2002–2019
A researcher in the Science corpus: 14 papers; trusts 42 researchers and is trusted by 59 researchers here, as inferred from the citation graph.
Subfields
- Cellular and Molecular Neuroscience — 55 citations into it; #224 of 2,998 by trust here, #129 by citations
- Cognitive Neuroscience — 54 citations into it; #1,503 of 10,852 by trust here, #746 by citations
- Molecular Biology — 43 citations into it; #4,726 of 17,773 by trust here, #1,649 by citations
- Neuroscience — 23 citations into it; #186 of 487 by trust here, #233 by citations
- Statistical and Nonlinear Physics — 17 citations into it; #3,245 of 10,127 by trust here, #219 by citations
- Computer Vision and Pattern Recognition — 16 citations into it
- Electrical and Electronic Engineering — 13 citations into it; #3,120 of 16,539 by trust here, #3,208 by citations
- Artificial Intelligence — 11 citations into it; #23,557 of 35,345 by trust here, #27,551 by citations
Whom Dmitri "Mitya" Chklovskii trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Erkki Oja
- Barbara Olshausen
- S. Shen-Orr
- Terence D. Sanger
- Andrzej S. Cichocki
- Mark Newman
- Henry Markram
- Ron Milo
- Sun-Yuan Kung
- Ralph Linsker
Who trusts Dmitri "Mitya" Chklovskii overall
- John A. Bullinaria
- Olaf Sporns
- Andreas Knoblauch
- E. Bullmore
- Markus Butz
- D. Bassett
- Martijn P. van den Heuvel
- Per Jesper Sjöström
- Rafael Yuste
- Paul Tonelli
Papers from Dmitri "Mitya" Chklovskii's perspective
Ordered by how strongly the researchers Dmitri "Mitya" Chklovskii trusts endorse each paper. Same corpus, a different order for every researcher.
- 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Sparse coding with an overcomplete basis set: a strategy employed by V1?
- Emergence of simple-cell receptive field properties by learning a sparse code for natural images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Neural networks and physical systems with emergent collective computational abilities.
Their papers
- Network motifs: simple building blocks of complex networks. (2002) — 7,486 citations
- Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits (2005) — 1,602 citations
- A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data (2015) — 120 citations
- A visual motion detection circuit suggested by Drosophila connectomics (2013) — 693 citations
- Wiring optimization in cortical circuits. (2002) — 498 citations
- Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks? (2017) — 86 citations
- CaImAn an open source tool for scalable calcium imaging data analysis (2019) — 780 citations
- Neuronal circuits underlying persistent representations despite time varying activity. (2012) — 172 citations
- Structural Properties of the Caenorhabditis elegans Neuronal Network (2009) — 942 citations
- Wiring optimization can relate neuronal structure and function. (2006) — 679 citations
- Search for computational modules in the C. elegans brain (2004) — 109 citations
- Cortical rewiring and information storage (2004) — 744 citations
- Networks Network Motifs : Simple Building Blocks of Complex (2002)
- Geometry and Structural Plasticity of Synaptic Connectivity (2002)