Research Group Matthias Kaschube – Science

to overview

Data-driven theoretical neuroscience

Research in my group focuses on the following three questions:

  1. Brain circuits are formed combining genetic and sensory information. This process involves complex network interactions on various levels. What is the interplay of the different factors leading to the development of cortical circuits?
  2. Cortical circuits produce rich patterns of electrical activity, which are thought to underlay perception, behavior and reasoning. What are the relevant features of these patterns supporting such cognitive phenomena? What circuit components do they depend on?
  3. Brains are remarkably robust. The functionality of cortical circuits is maintained despite significant turnover on cellular, subcellular and molecular levels. How is this robustness achieved?

In order to address these questions, my research employs mathematical, computational and machine learning techniques to study a variety of different systems, often in close collaboration with experimental neurobiologists. Ongoing projects include:

The role of cortical network interactions in early cortical development

This is in collaboration with David Fitzpatrick at Max-Planck Florida Institute (MPFI), Jupiter, USA. In our recent publication (Smith, Hein, Whitney et al., Nat Neurosci, 2018; https://rdcu.be/9PiY) we study in ferret visual cortex the role of network interactions in the development of the orientation preference map.

Neural variability, perceptual learning and cortical development

With David Fitzpatrick, MPFI. Our study (Smith, Sederberg et al., Nat Neurosci, 2015) shows that responses to identical visual stimuli are highly variable in the immature ferret visual cortex and that this response variability declines with age; these changes contribute significantly to a marked improvement in direction discriminability over development.

Assessing the stability of sensory representations in cortical networks.

This is in collaboration with Simon Rumpel at University Mainz. Our system of choice is the auditory cortex of habituated adult mice and we explore how robust sound responses are against ongoing fluctuations of neural connectivity that were observed in this system.

Exploiting skin patterns of cuttlefish

In our new study (Reiter et al., Nature, 2018; https://rdcu.be/9s2C) we are exploiting skin patterns of cuttlefish to obtain a precise and massively parallel readout of neural activity in a freely behaving organism. This is with Gilles Laurent at the Max-Planck Institute for Brain Research, Frankfurt, and involves image analysis and computational modeling.

The influence of recurrent connections on visual perception.

A computational model of primary visual cortex is devised and its functional properties studied using machine learning techniques.

Common design of orientation columns in the visual cortex

Understanding the fundamental difference between a visual cortex that has a functional columnar architecture (as in primates and carnivores) and one that lacks such an organization (as in rodent species), addressing both developmental and functional aspects.

Selected Talks

  • Kaschube M, Building cortical representations, Ferret brain meeting, San Diego  [link]
  • Trägenap S, Hein B, Whitney DE, Fitzpatrick D, Kaschube M (2022), Experience drives the development of novel, reliable cortical sensory representations from endogenous networks, Society for Neuroscience meeting [link] and Ferret brain meeting  [link], San Diego
  • Trägenap S, Hein B, Whitney DE, Fitzpatrick D, Kaschube M (2022), Experience drives the development of novel, reliable cortical sensory representations from endogenous networks, Bernstein Conference 2022, Berlin [link] [video]
  • Eppler JB, Aschauer, D Rumpel S, Kaschube M, Formation of associations by learning-induced biases in the ongoing dynamics of sensory representations Bernstein Conference 2021, Workshop: Maintaining function in the presence of ongoing change
  • Trägenap S, Hein B, Whitney DE, Smith GB, Fitzpatrick D, Kaschube M (2019), Co-refinement of network interactions and neural response properties in visual cortex. CNS 2019, Barcelona, Spain. [link]
  • Kaschube M (2018). Distributed network interactions and their emergence in developing neocortex. Spont 2018, Leiden, Netherlands. [link]
  • Kaschube M (2018). Sepia Skin Pattern Control and Development at Chromatophore Resolution. In: Quantitative Approaches to Naturalistic Behaviors, The Banbury Center, Cold Spring Harbor Laboratory, New York, USA. [link]
  • Kaschube M, Whitney DE, Smith GB, Hein B, Fitzpatrick D (2017). High cellular and columnar variability underlies the absence of early orientation selectivity. Neural Coding, Computation and Dynamics 2017, Capbreton, France. [link]
  • Hein B, Whitney DE, Smith GB, Fitzpatrick D, Kaschube M (2017). High cellular and columnar variability underlies the absence of early orientation selectivity. Bernstein Conference 2017, Göttingen. [link]
  • Hein B, Smith GB, Whitney DE, Hülsdunk P, Fitzpatrick D, Kaschube M (2017). The emergence of distributed functional networks in the early developing cortex. Cosyne Abstracts 2017, Salt Lake City, UT, USA. [link]
  • Hein B, Smith GB, Whitney DE, Hülsdunk P, Fitzpatrick D, Kaschube M (2016). Local circuits form long-ranging spontaneous correlations to build distributed sensory representations in the early developing cortex. Bernstein Conference 2016, Berlin.
  • Hein B, Neuschwander K, Smith GB, Whitney DE, Fitzpatrick D, Kaschube M (2015). Chronic study of spontaneous activity and orientation selectivity in visual cortex around eye opening. 11th Meeting of the German Neuroscience Society, 2015, Göttingen.
  • Hein B, Neuschwander K, Smith GB, Whitney DE, Fitzpatrick D, Kaschube M (2015) Born to be critical: Spontaneous activity in early cortex and its role in shaping sensory representations. BP 8.1, BP 378, German Physical Society (DPG) Frühjahrstagung, 2015, Berlin.
  • Kaschube M, Hein B, Neuschwander K, Smith GB, Whitney DE, Fitzpatrick D (2015). Early cortical spontaneous activity provides a scaffold for constructing sensory representations. Cosyne Abstracts 2015, Salt Lake City, UT, USA.

Selected Publications

  • Wehrheim, M. H., Faskowitz, J., Sporns, O., Fiebach, C. J., Kaschube*, M., & Hilger*, K. (2023). Few temporally distributed brain connectivity states predict human cognitive abilities. In NeuroImage (Vol. 277, p. 120246). Elsevier BV. [Link] * jointly directed work
  • Vogel, F. W., Alipek, S., Eppler, J.-B., Osuna-Vargas, P., Triesch, J., Bissen, D., Acker-Palmer, A., Rumpel, S., & Kaschube, M. (2023). Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging. In Scientific Reports (Vol. 13, Issue 1). Springer Science and Business Media LLC.[Link]
  • Vogel FW, Alipek S, Eppler JB, Triesch J, Bissen D, Acker-Palmer A, Rumpel S, Kaschube M. Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks. bioRxiv. 2023:2023-01.  [Link]
  • Trägenap S, Whitney DE, Fitzpatrick D, Kaschube M. Experience drives the development of novel, reliable cortical sensory representations from endogenously structured networks. bioRxiv. 2022:2022-11 [Link]
  • Wehrheim MH, Faskowitz J, Sporns O, Fiebach C, Kaschube M, Hilger K How much data do we need? Lower bounds of brain activation states to predict human cognitive ability. bioRxiv. 2022:2022-12. [Link]
  • Chambers AR, Aschauer DF, Eppler JB, Kaschube M, Rumpel S A stable sensory map emerges from a dynamic equilibrium of neurons with unstable tuning properties, Cerebral Cortex, 2022 [Link]
  • Aschauer DF#, Eppler JB#, Ewig L, Chambers A, Pokorny C, Kaschube M*, Rumpel S* (2022) Learning-induced biases in the ongoing dynamics of sensory representations predict stimulus generalization. Cell Reports 38(6):110340  #equal contribution; *jointly directed work. [Link]
  • Mulholland HN, Hein B, Kaschube M, Smith GB (2021)Tightly coupled inhibitory and excitatory functional networks in the developing primary visual cortex. Elife. 10:e72456. [Link]
  • Aschauer DF†, Eppler JB†, Ewig L, Chambers A, Pokorny C, Kaschube M*, Rumpel S*. (August 16, 2019) A Basis Set of Elementary Operations Captures Recombination of Neocortical Cell Assemblies During Basal Conditions and Learning. CELL-D-19-02189. Available at SSRN.†equally contributing author; *jointly directed work.
  • Harris, KD, Groh JM, DiCarlo J, Fries P, Kaschube M, Laurent G, MacLean J, McCormick D, Pipa G, Reynolds J, Schwartz A, Sejnowski T, Singer W, Vinck M, 2019. Functional Properties of Circuits, Cellular Populations, and Areas. In: The Neocortex, ed. W. Singer, T. J. Sejnowski and P. Rakic, pp. 223-265. Strüngmann Forum Reports, vol. 27, J. Lupp, series editor. Cambridge, MA: MIT Press.
  • Smith GB†, Hein B†, Whitney DE†, Fitzpatrick D*, Kaschube M*. (2018) Distributed network interactions and their emergence in developing neocortex. Nat Neurosci.; 21(11) 1600-1608 doi: 10.1038/s41593-018-0247-5. †equally contributing author; *jointly directed work. [link]
    Press release
    Link (MPFI): Movies of spontaneous activity and distributed networks in the visual cortex.
  • Reiter S, Hülsdunk P, Woo T, Lauterbach MA, Eberle JS, Akay LA, Longo A, Meier-Credo J, Kretschmer F, Langer JD, Kaschube M, Laurent G. (2018) Elucidating the control and development of skin patterning in cuttlefish. Nature; 562, 361-366. doi:10.1038/s41586-018-0591-3 [link]
    Press release
  • Kaschube M, Nelson III CA, Benasich AA, Buzsáki G, Gressens P, Hensch TK, Hübener M, Kobor MS, Singer W, Sur M. (2018) Early Childhood. In: Emergent Brain Dynamics: Prebirth to Adolescence, edited by A. A. Benasich and U. Ribary. Strüngmann Forum Reports, vol. 25, J. Lupp, series editor. Cambridge, MA: MIT Press
  • Smith GB, Sederberg A, Elyada YM, Van Hooser SD, Kaschube M*, Fitzpatrick D. (2015). The development of cortical circuits for motion discrimination. Nat Neurosci.; 18(2), 252-261. doi: 10.1038/nn.3921. *jointly directed work. [link]
  • Sederberg A, Kaschube M. (2015) Inhibition facilitates direction selectivity in a noisy cortical environment. J Comput Neurosci. doi: 10.1007/s10827-014-0538-0. [link]
  • Polyakov O, He B, Swan M, Shaevitz JW, Kaschube M, Wieschaus E. (2014) Passive mechanical forces control cell-shape change during Drosophila ventral furrow formation. Biophys J.;107(4):998-1010. doi: 10.1016/j.bpj.2014.07.013. [link]
  • Khan Z, Wang YC, Wieschaus EF, Kaschube M. (2014) Quantitative 4D analyses of epithelial folding during Drosophila gastrulation. Development.;141(14):2895-900. doi: 10.1242/dev.107730. [link]
  • Kaschube M (2014) Neural maps versus salt-and-pepper organization in visual cortex. Curr Opin Neurobiol 24:95–102. [link]
  • Reichl L, Heide D, Löwel S, Crowley JC, Kaschube M, Wolf F (2012) Coordinated Optimization of Visual Cortical Maps (II) Numerical Studies. PLoS Comput. Biol. 8(11):e1002756. [link]
  • Reichl L, Heide D, Löwel S, Crowley JC, Kaschube M, Wolf F (2012) Coordinated Optimization of Visual Cortical Maps (I) Symmetry-based Analysis. PLoS Comput. Biol. 8(11):e1002466. [link]
  • Gelbart MA, He B, Martin AC, Thiberge SY, Wieschaus EF, Kaschube M. (2012) Volume conservation principle involved in cell lengthening and nucleus movement during tissue morphogenesis. PNAS 109:19298–303. [link]
  • Keil W, Kaschube M, Schnabel M, Kisvarday ZF, Löwel S, Coppola DM, White LE, Wolf F (2012) Response to Comment on “Universality in the Evolution of Orientation Columns in the Visual Cortex“. Science 336:413. [link]
  • Nelson KS, Khan Z, Molnár I, Mihály J, Kaschube M, Beitel GJ (2012) Drosophila Src regulates anisotropic apical surface growth to control epithelial tube size. Nat Cell Biol 14(5):518–25. [link]
  • Wang YC, Khan Z, Kaschube M, Wieschaus EF (2012) Differential positioning of adherens junctions is associated with initiation of epithelial folding. Nature, 484:390–393. [link]
  • Macke JH, Gerwinn S, White LE, Kaschube M, Bethge M (2011) Gaussian process methods for estimating cortical maps. NeuroImage 56(2):570–81. [link]
  • Kaschube M, Schnabel M, Löwel S, Coppola DM, White LE, Wolf F (2010) Universality in the evolution of orientation columns in the visual cortex. Science 330:1113–1116. [link]
  • Keil W, Schmidt K-F, Löwel S, Kaschube M (2010) Reorganization of columnar architecture in the growing visual cortex. PNAS 107:12293–12298. [link]
  • Martin AC, Gelbart M, Fernandez-Gonzalez R, Kaschube M, Wieschaus EF (2010) Integration of contractile forces during tissue invagination. JCB 188:735–749. [link]
  • Doubrovinski K, Polyakov O, Kaschube M (2010) A mesoscopic description of contractile cytoskeletal meshworks. EPJE 33:105–110. [link]
  • Martin AC, Kaschube M, Wieschaus EF (2009) Pulsed contractions of an actin–myosin network drive apical constriction. Nature 457:495–499. [link]
  • Kaschube M, Schnabel M, Wolf F, Löwel S (2009) Interareal coordination of columnar architectures during visual cortical development. PNAS 106:17205–17210. [link]
  • Macke JH, Gerwin S, White LE, Kaschube M, Bethge M (2009) Bayesian estimation of orientation preference maps. NIPS 22. [link]
  • Kaschube M, Schnabel M, Wolf F (2008) Self-organization and the selection of pinwheel density in visual cortical development. NJP 10:015009. [link]
  • Schnabel M, Kaschube M, Wolf F (2008) Pinwheel stability, pattern selection and the geometry of visual space. [link]
  • Schnabel M, Kaschube M, Loewel S and Wolf F (2007) Random Waves in the Brain: Symmetries and Defect Generation in the Visual Cortex. EPJ ST 145:137–157. [link]
  • Kaschube M, Wolf F, Puhlmann M, Rathjen S, Schmidt KF, Geisel T and Loewel S (2003) The pattern of ocular dominance columns in cat primary visual cortex: Intra- and interindividual variability of column spacing and its dependence on genetic background. EJN 18:3251–3266. [link]
  • Kaschube M, Wolf F, Geisel T, Löwel S (2002) Genetic influence on quantitative features of neocortical architecture. J Neurosc. 22:7206–7217. [link]
  • Kaschube M, Wolf F, Geisel T, Löwel S (2001) The prevalence of colinear contours in the real world. Neurocomputing 38–40:1335–1339. [link]
  • Kaschube M, Wolf F, Geisel T, Löwel S (2000) Quantifying the variability of patterns of orientation domains in the visual cortex of cats. Neurocomputing 32–33:415–423. [link]

Software

Network model of early visual cortex [Smith, Hein, Whitney et al., Nat Neurosci, 2018][link].

Tools for analysing Calcium imaging data [Smith, Hein, Whitney et al., Nat Neurosci, 2018][link].