Berens Lab

Data Science for Vision Research

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2021

  1. Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P. (2021). Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data. Journal of the Royal Statistical Society: Series C (Applied Statistics). link
  2. Laturnus, S.C., Berens P. MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space (2021) Proceedings of the International Conference on Machine Learning link
  3. Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., Ayhan, M. S. (2021) Interpretable gender classification from retinalfundus images using BagNets, MICCAI link

2020

  1. Federico Scala, Dmitry Kobak, Matteo Bernabucci, Yves Bernaerts, Cathryn René Cadwell, Jesus Ramon Castro, Leonard Hartmanis, Xiaolong Jiang, Sophie Laturnus, Elanine Miranda, Shalaka Mulherkar, Zheng Huan Tan, Zizhen Yao, Hongkui Zeng, Rickard Sandberg, Philipp Berens & Andreas S. Tolias. Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature (2020). link
  2. Jonathan Oesterle, Christian Behrens, Cornelius Schröder, Thoralf Hermann, Thomas Euler, Katrin Franke, Robert G Smith, Günther Zeck, Philipp Berens. (2020) Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics. eLife;9:e54997 link
  3. Klaudia P. Szatko, Maria M. Korympidou, Yanli Ran, Philipp Berens, Deniz Dalkara, Timm Schubert, Thomas Euler & Katrin Franke. (2020) Neural circuits in the mouse retina support color vision in the upper visual field. Nat Commun 11, 3481. link
  4. Murat Seçkin Ayhan, Laura Kühlewein, Gulnar Aliyeva, Werner Inhoffen, Focke Ziemssen, Philipp Berens. Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection. (2020) Medical Image Analysis, Volume 64, 101724. link
  5. Berens P, Waldstein SM, Ayhan MS, Kümmerle L, Agostini H, Stahl A, Ziemssen F. (2020) Potential of methods of artificial intelligence for quality assurance. Ophthalmologe 117, 320–325 link
  6. Yoshimatsu, T., Schröder, C., Nevala, N. E., Berens, P., & Baden, T. (2020). Fovea-like Photoreceptor Specializations Underlie Single UV Cone Driven Prey-Capture Behavior in Zebrafish. Neuron. link
  7. Meding, K., Bruijns, S. A., Schölkopf, B., Berens, P., & Wichmann, F. A. (2020). Phenomenal Causality and Sensory Realism. i-Perception, 11(3), 2041669520927038. link
  8. Ran, Y., Huang, Z., Baden, T., Schubert, T., Baayen, H., Berens, P., Franke, K. & Euler, T. (2020). Type-specific dendritic integration in mouse retinal ganglion cells. Nature Communications, 11(1), 1-15. link
  9. Laturnus, S., Kobak, D., & Berens, P. (2019). A systematic evaluation of neural morphology representations for cell type discrimination. Neuroinformatics, link
  10. Berens, P., Waldstein, S.M., Ayhan, M.S. et al. (2020) Potenzial von Methoden der künstlichen Intelligenz für die Qualitätssicherung. Ophthalmologe. link
  11. Höfling, L., Oesterle, J., Berens, P. et al. (2020) Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina. Sci Rep 10, 5248. link
  12. Cadwell, C.R., Scala, F., Fahey, P.G., Kobak, D., Mulherkar, S., Sinz, F.H., Papadopoulos, S., Tan, Z.H., Johnsson, P., Hartmanis, L., Li, S., Cotton, R.J., Tolias, K.F., Sandberg, R., Berens, P., Jiang, X., Savas Tolias, A. (2020) Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex. eLife 2020;9:e52951 link
  13. Zhao, Z., Klindt, D.A., Maia Chagas, A., Szatko, K.P., Rogerson, L., Protti, D.A., Behrens, C., Dalkara, D., Schubert, T., Bethge, M., Franke, K., Berens, P., Ecker, A.S. & Euler, T. (2020) The temporal structure of the inner retina at a single glance. Sci Rep 10, 4399. link
  14. Power, MJ, Rogerson, LE, Schubert, T, Berens, P, Euler, T, Paquet‐Durand, F. (2020) Systematic spatiotemporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration. J Comp Neurol.; 528: 1113– 1139. link

2019

  1. Grote, T., & Berens, P. (2019). On the ethics of algorithmic decision-making in healthcare. Journal of Medical Ethics. link
  2. Kobak, D., & Berens, P. (2019). The art of using t-SNE for single-cell transcriptomics. Nature communications, 10(1), 1-14. link
  3. Baden, T., Euler, T., & Berens, P. (2019). Understanding the retinal basis of vision across species. Nature Reviews Neuroscience, 1-16. link
  4. Scala, F., Kobak, D., Shan, S., Bernaerts, Y., Laturnus, S., Cadwell, C. R., Papadopoulos, S., Patel, S. S., Sandberg, R., Berens, P., Jiang, X., Tolias, A. S. (2019). Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas. Nature communications, 10(1), 1-12. link
  5. Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P. (2019). Bayesian hypothesis testing and experimental design for two-photon imaging data. PLoS computational biology, 15(8), e1007205. link
  6. Schröder, C., James, B., Lagnado, L., & Berens, P. (2019). Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse. In Advances in Neural Information Processing Systems (pp. 7068-7078). link
  7. Kobak, D., Pardo-Vazquez, J. L., Valente, M., Machens, C. K., & Renart, A. (2019). State-dependent geometry of population activity in rat auditory cortex. eLife, 8, e44526. link
  8. Bellet M. E., Bellet J., Nienborg H., Hafed Z. M., Berens P. "Human-level saccade detection performance using deep neural networks" (2019) Journal of Neurophysiology 121:2, 646-661, link
  9. Román Rosón M., Bauer Y., Kotkat A. H., Berens P., Euler T., Busse L. "Mouse dLGN Receives Functional Input from a Diverse Population of Retinal Ganglion Cells with Limited Convergence" (2019) Neuron, link
  10. Dhande O. S., Stafford B. K., Franke K., El-Danaf R., Percival K. A., Phan A. H., Li P., Hansen B. J., Nguyen P. L., Berens P., Taylor W. R., Callaway E., Euler T., Huberman A. D. "Molecular fingerprinting of On-Off direction selective retinal ganglion cells across species and relevance to primate visual circuits" (2019) J Neurosci; link

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2018

  1. Zimmermann M. J. Y., Nevala N. E., Yoshimatsu T., Osorio D., Nilsson D. E., Berens P., Baden T. "Zebrafish Differentially Process Color across Visual Space to Match Natural Scenes." (2018) Curr Biol. Epub 2018 Jun 21. link
  2. Berens P., Freeman J., Deneux T., Chenkov N., McColgan T., Speiser A., Macke J. H., Turaga S. C., Mineault P., Rupprecht P., Gerhard S., Friedrich R. W., Friedrich J., Paninski L., Pachitariu M., Harris K. D., Bolte B., Machado T. A., Ringach D., Stone J., Rogerson L. E., Sofroniew N. J., Reimer J., Froudarakis E., Euler T., Román Rosón M., Theis L., Tolias A. S., Bethge M. "Community-based benchmarking improves spike rate inference from two-photon calcium imaging data." (2018) PLoS Comput Biol. eCollection 2018 May link
  3. Diamantaki M., Coletta S., Nasr K., Zeraati R., Laturnus S., Berens P., Preston-Ferrer P., Burgalossi A. "Manipulating Hippocampal Place Cell Activity by Single-Cell Stimulation in Freely Moving Mice." (2018) Cell Rep. link
  4. Ayhan M. S., Berens P. "Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks." (2018) Medical Imaging with Deep Learning. link
  5. Subramaniyan M., Ecker A. S., Patel S. S., Cotton R. J., Bethge M., Pitkow X., Berens P., Tolias A ."Faster processing of moving compared to flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion." (2018) J Neurophysiol. [Epub ahead of print] link

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2017

  1. Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017). Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports, 7(1), 17816, link
  2. C. Chapot, C. Behrens, L. E. Rogerson, T. Baden, S. Pop, P. Berens, T. Euler, T. Schubert (2017): Local Signals in Mouse Horizontal Cell Dendrites, Current Biology, link
  3. M. Nonnenmacher, C. Behrens, P. Berens, M. Bethge, JH Macke (2017): Signatures of criticality arise from random subsampling in simple population models, PLoS Comput Biol, 10(13),  e1005718, link
  4. Rogerson, L. E., C. Behrens, T. Euler, P. Berens, T. Schubert (2017): Connectomics of synaptic  microcircuits: lessons from the outer retina. Journal of Physiology, Review, link
  5. Franke* K., P. Berens*, T. Schubert, M. Bethge, T. Euler, T. Baden (2017): Inhibition decorrelates visual feature representation in the inner retina. Nature, 542, 439-444  link

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2016

  1. Behrens* C., T. Schubert*, S. Haverkamp, T. Euler, P. Berens (2016): Connectivity map of bipolar cells and photoreceptors in the mouse retina. eLife, link
  2. Maria Diamantaki Markus Frey Philipp Berens Patricia Preston-Ferrer Andrea Burgalossi (2016) Sparse activity of identified dentate granule cells during spatial exploration.  eLife 2016;5:e20252 link
  3. Jiang X, S. Shen, F. Sinz, J. Reimer, C. R. Cadwell, P. Berens, A. S. Ecker, S. Patel, G. Denfield, E. Froudarakis, S. Li, E. Walker, A. S. Tolias (2016): Response to Comment on “Principles of connectivity among morphologically defined cell types in adult neocortex”, Science, 353 (6304), 1108 link
  4. L. Theis*, P. Berens*+, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias, M. Bethge+ (2016): Benchmarking spike rate inference in population calcium imaging, Neuron, 90(3), 471-482, link
  5. Baden T*, P Berens*, K Franke*, M Rezac, M Bethge, T Euler$: The functional diversity of retinal ganglion cells in the mouse (2016), Nature,529, 345-350, link
  6. Cadwell CR, A Palasantza, X Jiang, P Berens, Q Deng, M Yilmaz, J Reimer, S Shen, M Bethge, KF Tolias, R Sandberg, AS Tolias: Morphological, electrophysiological and transcriptomic profiling of single neurons using Patch-seq (2016), Nature Biotechnology, 34, 199-203, link

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2015

  1. Jiang X, S Shen, CR Cadwell CR, P Berens, F Sinz, AS Ecker, S Patel, AS Tolias: Principles of connectivity among morphologically defined cell types in adult neocortex (2015), Science, 350 (6264), aac9462 link

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2014

  1. Froudarakis, E., P. Berens+, A. S. Ecker, R. J. Cotton, F. H. Sinz, D. Yatsenko, P. Saggau, M. Bethge, A. S. Tolias+ (2014): Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness, Nature Neuroscience, PDF
  2. Ecker, A. S., P. Berens, R. J. Cotton, M. Subramaniyan, G. H. Denfield, C. R. Cadwell, S. M. Smirnakis, M. Bethge, A. S. Tolias (2014): State-dependence of noise correlations in macaque primary visual cortex, Neuron, 81(1), 235-248 PDF

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2013

  1. Subramanian, M., A. S. Ecker, P. Berens, A. S. Tolias (2013): Macaque monkeys perceive the flash-lag illusion. PLoS ONE, 8(3): e58788 PDF
  2. Baden, T., P. Berens, M. Bethge, and T. Euler (2013): Spikes in Mammalian Bipolar Cells Support Temporal Layering of the Inner Retina. Current Biology, 23(1), 48-52 PDF

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2012 and earlier

  1. Berens, P., A. S. Ecker, R. J. Cotton, W. J. Ma, M. Bethge, and A. S. Tolias (2012): A fast and simple population code for orientation in primate V1. The Journal of Neuroscience , 32(31), 10618-10626 PDF
  2. Ecker, A. S., P. Berens, A. S. Tolias, and M. Bethge (2011): The effect of noise correlations in populations of diversely tuned neurons. The Journal of Neuroscience, 31(40), 14272-14283 PDF
  3. Berens, P., A. S. Ecker, S. Gerwinn, A. S. Tolias and M. Bethge (2011): Reassessing optimal neural population codes with neurometric functions. Proceedings of the National Academy of Sciences 108(11), 4423-4428 PDF Supplement
  4. Ecker, A. S., P. Berens, G. A. Keliris, M. Bethge, N. K. Logothetis and A. S. Tolias (2010): Decorrelated Firing in Cortical Microcircuits. Science, 327(5965), 584-587 PDF Supplement
  5. Berens, P. (2009): CircStat: A Matlab Toolbox for Circular Statistics. Journal of Statistical Software 31(10), 1-21 PDF
  6. Macke*, J. H., P. Berens*, A. S. Ecker, A. S. Tolias and M. Bethge (2009): Generating Spike Trains with Specified Correlation Coefficients. Neural Computation 21(2), 397-423 PDF
  7. Berens, P.,S. Gerwinn, A. S. Ecker and M. Bethge (2009): Neurometric function analysis of population codes. Advances in Neural Information Processing Systems 22: Proceedings of the 2009 Conference, 90-98. (Eds.) Bengio, Y., D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta, MIT Press, Cambridge, MA, USA PDF
  8. Gerwinn, S. , P. Berens, and M. Bethge (2009): A joint maximum-entropy model for binary neural population patterns and continuous signals. Advances in Neural Information Processing Systems 22: Proceedings of the 2009 Conference, 620-628. (Eds.) Bengio, Y., D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta, MIT Press, Cambridge, MA, USA PDF
  9. Berens, P., G. A. Keliris, A. S. Ecker, N. K. Logothetis and A. S. Tolias (2008): Feature selectivity of the gamma-band of the local field potential in primate primary visual cortex. Frontiers in Neuroscience 2(2), 199-207; Focused Review PDF
  10. Berens, P., G. A. Keliris, A. S. Ecker, N. K. Logothetis and A. S. Tolias (2008): Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex. Frontiers in Systems Neuroscience 2(2) PDF
  11. Bethge, M. and P. Berens (2008): Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference, 97-104. (Eds.) Platt, J. C., D. Koller, Y. Singer, S. Roweis, MIT Press, Cambridge, MA, USA PDF

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Other

  1. Janschewski, J., P. Berens, C. Käppler (2014): Psychisch belastete Kinder und Jugendliche im Schulkontext – eine empirische Analyse schulischer Problemlagen anhand von Schulakten einer Klinikschule. Zeitschrift für Heilpädagogik (in German), 368-378
  2. Berens, P. (2013): The programming language of the brain [in german]. Bild der Wissenschaft (Supplement), 10, 14-17 PDF
  3. T. Baden, L. P. Godino, S. Yusuf, and P. Berens(2013): Neurowissenschaften in Afrika – Kooperationen und Perspektiven. Neuroforum, 19(2), 73-74 PDF
  4. Macke, J., P. Berens, and M. Bethge (2011): Statistical analysis of multi-cell recordings: linking population coding models to experimental data. Frontiers in Computational Neuroscience, 5(35) PDF
  5. Berens, P.,N. K. Logothetis and A. S. Tolias (in press): Local field potentials, BOLD and spiking activity – relationships and physiological mechanisms. In: Understanding visual population codes – towards a common multivariate framework for cell recording and functional imaging, 599-624.  (Eds.) Kriegeskorte, N. and G. Kreiman, MIT Press, Cambridge, MA, USA PDF Link

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* denotes shared first authors, + denotes shared corresponding author

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