D. Kobak

发表

Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons

Jeremiah Y. Cohen, Christian K. Machens, E. Callaway, 2016, Neuron.

Author response: Demixed principal component analysis of neural population data

Naoshige Uchida, Wieland Brendel, Christos Constantinidis, 2016 .

State-dependent geometry of population activity in rat auditory cortex

Jose L. Pardo-Vazquez, Christian K. Machens, A. Renart, 2018, bioRxiv.

State-dependent geometry of population activity in rat auditory cortex

A. Renart, C. Machens, J. L. Pardo-Vazquez, 2018, bioRxiv.

Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas

Saumil S. Patel, Philipp Berens, C. Cadwell, 2019, Nature Communications.

Neocortical layer 4 in adult mouse differs in major cell types and circuit organization between primary sensory areas

Philipp Berens, C. Cadwell, A. Tolias, 2018, bioRxiv.

Distributed and Mixed Info rmation in Monosynaptic Inputs to Dopamine Neurons Highlights

Jeremiah Y. Cohen, Christian K. Machens, E. Callaway, 2016 .

Adaptation Paths to Novel Motor Tasks Are Shaped by Prior Structure Learning

Dmitry Kobak, Carsten Mehring, C. Mehring, 2012, The Journal of Neuroscience.

Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex

Paul G. Fahey, Philipp Berens, C. Cadwell, 2020, eLife.

Cell type composition and circuit organization of neocortical radial clones

Paul G. Fahey, Philipp Berens, R. Cotton, 2019, bioRxiv.

Phenotypic variation of transcriptomic cell types in mouse motor cortex

Philipp Berens, C. Cadwell, A. Tolias, 2020, Nature.

A multimodal cell census and atlas of the mammalian primary motor cortex

Satrajit S. Ghosh, Nicholas N. Foster, Evan Z. Macosko, 2020, Nature.

Contrastive learning unifies t-SNE and UMAP

F. Hamprecht, D. Kobak, Sebastian Damrich, 2022, ArXiv.

Patch-seq: Past, Present, and Future

C. Cadwell, D. Kobak, S. Tripathy, 2021, The Journal of Neuroscience.

Integer percentages as electoral falsification fingerprints

D. Kobak, M. Pshenichnikov, Sergey A. Shpilkin, 2014, 1410.6059.

Unsupervised visualization of image datasets using contrastive learning

Jan Boehm, D. Kobak, P. Berens, 2022, ArXiv.

Attraction-Repulsion Spectrum in Neighbor Embeddings

Philipp Berens, J. Bohm, D. Kobak, 2020, J. Mach. Learn. Res..

Suspect peaks in Russia's “referendum” results

D. Kobak, M. Pshenichnikov, Sergey A. Shpilkin, 2020, Significance.

The art of using t-SNE for single-cell transcriptomics

Philipp Berens, Dmitry Kobak, Philipp Berens, 2018, Nature Communications.

Demixed principal component analysis of neural population data

Naoshige Uchida, Wieland Brendel, Christos Constantinidis, 2014, eLife.

Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

Christian K. Machens, R. Romo, Wieland Brendel, 2014, 1410.6031.

The World Mortality Dataset: Tracking excess mortality across countries during the COVID-19 pandemic

D. Kobak, A. Karlinsky, 2021, medRxiv.

Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

D. Kobak, A. Karlinsky, 2021, eLife.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

Philipp Berens, D. Kobak, Jan Lause, 2020, Genome Biology.

UMAP does not preserve global structure any better than t-SNE when using the same initialization

George C. Linderman, Dmitry Kobak, D. Kobak, 2019, bioRxiv.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

Philipp Berens, D. Kobak, Jan Lause, 2021, Genome Biology.

Sparse bottleneck neural networks for exploratory non-linear visualization of Patch-seq data

Philipp Berens, D. Kobak, Yves Bernaerts, 2020, 2006.10411.

Initialization is critical for preserving global data structure in both t-SNE and UMAP

D. Kobak, G. Linderman, 2021, Nature Biotechnology.

Unsupervised visualization of image datasets using contrastive learning

Philipp Berens, Jan Boehm, D. Kobak, 2022, ArXiv.

A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination

Philipp Berens, Sophie Laturnus, Dmitry Kobak, 2019, Neuroinformatics.

A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum pdf

Jan Niklas Böhm, Philipp Berens, Dmitry Kobak, 2020, ArXiv.

Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data

Marissa A. Weis, Philipp Berens, A. Tolias, 2021, Journal of the Royal Statistical Society: Series C (Applied Statistics).

A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination

Philipp Berens, D. Kobak, S. Laturnus, 2020, Neuroinformatics.

Sparse reduced-rank regression for exploratory visualization of multimodal data sets

Philipp Berens, Federico Scala, Dmitry Kobak, 2018, bioRxiv.

Phenotypic variation within and across transcriptomic cell types in mouse motor cortex

Hongkui Zeng, Philipp Berens, Federico Scala, 2020, bioRxiv.

Excess Mortality Reveals Covid's True Toll in Russia

D. Kobak, 2021, Significance.

Distinct organization of two cortico-cortical feedback pathways

Andreas S. Tolias, Federico Scala, Zheng Huan Tan, 2020, bioRxiv.

Conflicting COVID-19 excess mortality estimates

D. Kobak, Jonas Schöley, A. Karlinsky, 2023, The Lancet.

Combined statistical-mechanistic modeling links ion channel genes to physiology of cortical neuron types

J. Macke, Philipp Berens, A. Tolias, 2023, bioRxiv.

Motor skill learning decreases movement variability and increases planning horizon.

C. Mehring, J. Diedrichsen, D. Kobak, 2022, Journal of neurophysiology.

Motor skill learning decreases motor variability and increases planning horizon

Jörn Diedrichsen, Carsten Mehring, Luke Bashford, 2018 .

Motor skill learning leads to the increase of planning horizon

Carsten Mehring, Joern Diedrichsen, Luke Bashford, 2018 .

Unsupervised visualization of image datasets using contrastive learning

Philipp Berens, Jan Boehm, D. Kobak, 2022, ICLR.

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations

Stefan Steinerberger, Philipp Berens, Yuval Kluger, 2019, ECML/PKDD.

Putin's peaks: Russian election data revisited

D. Kobak, M. Pshenichnikov, Sergey A. Shpilkin, 2018, Significance.

Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization

D. Kobak, Jonathan Lomond, B. Sanchez, 2018, 1805.10939.

Interpretable Gender Classification from Retinal Fundus Images using BagNets

Philipp Berens, D. Kobak, M. Ayhan, 2021, medRxiv.

Author response: Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

D. Kobak, A. Karlinsky, 2021 .

Statistical anomalies in 2011-2012 Russian elections revealed by 2D correlation analysis

Dmitry Kobak, Maxim S. Pshenichnikov, D. Kobak, 2012, 1205.0741.

Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs

Philipp Berens, D. Kobak, L. Hartmanis, 2023, bioRxiv.

The landscape of biomedical research

Benjamin M. Schmidt, Philipp Berens, D. Kobak, 2023, bioRxiv.

Two-dimensional visualization of large document libraries using t-SNE

Philipp Berens, D. Kobak, Rita González-Márquez, 2022, TAG-ML.

From $t$-SNE to UMAP with contrastive learning

F. Hamprecht, D. Kobak, Sebastian Damrich, 2022, ICLR.

Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data pdf

Philipp Berens, Dmitry Kobak, Yves Bernaerts, 2020, ArXiv.

Underdispersion: A statistical anomaly in reported Covid data

D. Kobak, 2022, Significance.

Persistent homology for high-dimensional data based on spectral methods

D. Kobak, Sebastian Damrich, Philipp Berens, 2023, ArXiv.

Self-supervised Visualisation of Medical Image Datasets

D. Kobak, Jan Niklas Böhm, I. V. Nwabufo, 2024, ArXiv.