Dmitry Kobak
Title
Cited by
Cited by
Year
Demixed principal component analysis of neural population data
D Kobak, W Brendel, C Constantinidis, CE Feierstein, A Kepecs, ...
Elife 5, e10989, 2016
2092016
Distributed and mixed information in monosynaptic inputs to dopamine neurons
J Tian, R Huang, JY Cohen, F Osakada, D Kobak, CK Machens, ...
Neuron 91 (6), 1374-1389, 2016
1422016
The art of using t-SNE for single-cell transcriptomics
D Kobak, P Berens
Nature communications 10 (1), 1-14, 2019
922019
Statistical fingerprints of electoral fraud?
D Kobak, S Shpilkin, MS Pshenichnikov
Significance 13 (4), 20-23, 2016
37*2016
Statistical anomalies in 2011-2012 Russian elections revealed by 2D correlation analysis
D Kobak, S Shpilkin, MS Pshenichnikov
arXiv preprint arXiv:1205.0741, 2012
352012
Integer percentages as electoral falsification fingerprints
D Kobak, S Shpilkin, MS Pshenichnikov
The Annals of Applied Statistics, 54-73, 2016
332016
Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas
F Scala, D Kobak, S Shan, Y Bernaerts, S Laturnus, CR Cadwell, ...
Nature communications 10 (1), 1-12, 2019
28*2019
Adaptation paths to novel motor tasks are shaped by prior structure learning
D Kobak, C Mehring
Journal of Neuroscience 32 (29), 9898-9908, 2012
192012
Phenotypic variation of transcriptomic cell types in mouse motor cortex
F Scala, D Kobak, M Bernabucci, Y Bernaerts, CR Cadwell, JR Castro, ...
Nature, 1-7, 2020
11*2020
The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
D Kobak, J Lomond, B Sanchez
Journal of Machine Learning Research 21 (169), 1-16, 2020
10*2020
UMAP does not preserve global structure any better than t-SNE when using the same initialization
D Kobak, GC Linderman
bioRxiv, 2019
92019
Putin's peaks: Russian election data revisited
D Kobak, S Shpilkin, MS Pshenichnikov
Significance 15 (3), 8-9, 2018
72018
Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex
CR Cadwell, F Scala, PG Fahey, D Kobak, S Mulherkar, FH Sinz, ...
Elife 9, e52951, 2020
6*2020
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
D Kobak, G Linderman, S Steinerberger, Y Kluger, P Berens
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
62019
A systematic evaluation of interneuron morphology representations for cell type discrimination
S Laturnus, D Kobak, P Berens
Neuroinformatics 18 (4), 591-609, 2020
42020
State-dependent geometry of population activity in rat auditory cortex
D Kobak, JL Pardo-Vazquez, M Valente, CK Machens, A Renart
Elife 8, e44526, 2019
32019
Sparse reduced-rank regression for exploratory visualization of paired multivariate datasets
D Kobak, Y Bernaerts, MA Weis, F Scala, A Tolias, P Berens
bioRxiv, 302208, 2020
2*2020
Distinct organization of two cortico-cortical feedback pathways
S Shen, X Jiang, F Scala, J Fu, P Fahey, D Kobak, Z Tan, J Reimer, ...
bioRxiv, 2020
12020
Motor skill learning leads to the increase of planning horizon
L Bashford, D Kobak, J Diedrichsen, C Mehring
1*2018
Suspect peaks in Russia's “referendum” results
D Kobak, S Shpilkin, MS Pshenichnikov
Significance 17 (5), 8-9, 2020
2020
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Articles 1–20