Y. Zang, S. Hong and E. De Schutter: Firing rate-dependent phase responses of Purkinje cells support transient oscillations. eLife 9: 60692.
I. Wichert, S. Jee, E. De Schutter, S. Hong: Pycabnn: Efficient and extensible software to construct an anatomical basis for a physiologically realistic neural network model. Frontiers in Neuroinformatics 14: 31.
Y. Zang and E. De Schutter: Climbing Fibers Provide Graded Error Signals in Cerebellar Learning. Frontiers in Systems Neuroscience 13: 46.
E. De Schutter: Fallacies of Mice Experiments. Neuroinformatics 17: 181-183.
N. Vrieler, S. Loyola, Y. Yarden-Rabinowitz, J. Hoogendorp, N. Medvedev, T.M. Hoogland, C.I. De Zeeuw, E. De Schutter, Y. Yarom, M. Negrello, B. Torben-Nielsen, and M.Y. Uusisaari: Variability and directionality of inferior olive neuron dendrites revealed by detailed 3D characterization of an extensive morphological library. Brain Structure and Function: 224: 1677–1695.
S.O. Verduzco Flores and E. De Schutter: Draculab: A Python simulator for firing rate neural networks with delayed adaptive connections. Frontiers in Neuroinformatics 13:18.
C.G. Zamora Chimal and E. De Schutter: Ca2+ requirements for Long-Term Depression are frequency sensitive in Purkinje Cells. Frontiers in Molecular Neuroscience 11: 438.
Y. Zang, S. Dieudonné and E. De Schutter: Voltage- and Branch-specific Climbing Fiber Responses in Purkinje Cells. Cell Reports 24: 1536–1549.
J. Myung*, C. Schmal*, S. Hong*, Y. Tsukizawa, P. Rose, Y. Zhang, M. Holtzman, E. De Schutter, H. Herzel, G. Bordyugov and T. Takumi: The Choroid Plexus is an Important Circadian Clock Component. Nature Communications 9: 1062.
A.R. Gallimore, T. Kim, K. Tanaka-Yamamoto and E. De Schutter: Switching on depression and potentiation in the cerebellum. Cell Reports 22: 722-733.
E. De Schutter: Deep learning and computational neuroscience. Neuroinformatics 16: 1-2.
S. Nanda, H. Chen, R. Das, S. Bhattacharjee, H. Cuntz, B. Torben-Nielsen, H. Peng, D. Cox, E. De Schutter, and G. Ascoli: Design and implementation of multi-signal and time-varying neural reconstructions. Scientific Data 5:170207.
J. Myung and S.D. Pauls: Encoding seasonal information in a two-oscillator model of the multi-oscillator circadian clock. European Journal of Neuroscience 48: 2718-2727 (2018).
S.K. Sudhakar*, S. Hong*, I. Raikov, R. Publio, C. Lang, T. Close, D. Guo, M. Negrello and E. De Schutter: Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer. PLoS Computational Biology 13: e1005754.
C. Schmal, J. Myung, H. Herzel and G. Bordyugov: Using Moran’s I to analyze spatio-temporal pattern formation in neural imaging data. Bioinformatics 33:3072-3079.
W. Chen and E. De Schutter: Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers. Frontiers in Neuroinformatics 11: 13.
I. Hepburn, A. Jain, H. Gangal, Y. Yamamoto, K. Tanaka-Yamamoto and E. De Schutter: A model of induction of cerebellar Long-Term Depression including RKIP inactivation of Raf and MEK. Frontiers in Molecular Neuroscience 10: 19.
W. Chen and E. De Schutter: Time to bring single neuron modeling into 3D. Neuroinformatics 15: 1-3.
E. De Schutter: Neuroinformatics for degenerate brains. Neuroinformatics 14: 1-3.
E. De Schutter: The Missing Piece of the Puzzle: Neuroinformatics at the Bench. Neuroinformatics 13: 131-132.
B. Torben-Nielsen and E. De Schutter: Context-aware modeling of neuronal morphologies. Frontiers in Neuroanatomy 8: 92.
E. De Schutter: The dangers of plug-and-play simulation using shared models. Neuroinformatics 12: 227-228.
K. Veys, D. J. Snyders and E. De Schutter: Kv3.3b expression defines the shape of the complex spike in the Purkinje cell. Frontiers in Cellular Neuroscience 7: 205.
C. Simon, W. Chen, I. Hepburn and E. De Schutter: The role of dendritic spine morphology in the compartmentalization and delivery of surface receptors. Journal of Computational Neuroscience 36: 483-497.
I. Hepburn, R. Cannon and E. De Schutter: Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS. Frontiers in Computational Neuroscience 7: 129.
H. Anwar*, I. Hepburn*, H. Nedelescu, W. Chen and E. De Schutter: Stochastic calcium mechanisms cause dendritic calcium spike variability. Journal of Neuroscience 33: 15848-15867.
S. Ratté, S. Hong, E. De Schutter and S.A. Prescott: Impact of neuronal properties on network coding: Roles of spike initiation dynamics and robust synchrony transfer. Neuron 78: 758-772.
S. Huang and M.Y. Uusisaari: Physiological temperature during brain slicing enhances the quality of acute slice preparations. Frontiers in Cellular Neuroscience 7: 48.
E. De Schutter: Collaborative modeling in neuroscience: time to go open model? Neuroinformatics 11: 135-136.
E. De Schutter: The importance of stochastic signaling processes in the induction of long-term synaptic plasticity. Neural Networks 47: 3-10.
M.R. Diaz, A. Wadleigh, S. Kumar, E. De Schutter and C.F. Valenzuela: Na+/K+-ATPase inhibition partially mimics the ethanol-induced increase of the Golgi cell-dependent component of the tonic GABAergic current in rat cerebellar granule cells. PLoS One 8:e55673.
Note: this page lists only the Unit's publications in international journals, conference proceedings and books. We do not list abstracts or PhD theses. A complete list of Erik De Schutter's publications can be found at the University of Antwerp (with reprints) and at ResearcherID.
* these authors contributed equally to this work