Research

Our research is completely theoretical and uses computational methods to model the biochemical and electrophysiological behavior of neurons and microcircuits and motor control of limb movements. We also perform analysis of experimental data provided by external collaborators and develop software.

Molecular Modeling

steps-iconWe use molecular modeling methods to study how signaling pathways involved with synaptic plasticity are influenced by the detailed morphology of neurons and by the stochastic behavior of the reactions due to the small number of molecules. To support this work we develop a simulator program called STEPS (1) that implements the spatial Gillespie SSA in a tetrahedral grid and is meant to simulate molecular events in spines or small parts of a neuronal dendrite or axon. An extension of STEPS allows for calculation of the membrane potential on the same tetrahedral mesh so that voltage-gated ion channels can also be simulated. Recently we have succeeded in effectively parallelizing the spatial SSA (2) and STEPS (3), this allows us to simulate a complete neuron. 

We have been using STEPS to study anomalous diffusion in spiny dendrites (4) and the stochasticity of induction of cerebellar long-term depression in Purkinje cells (5). More recently we have developed an integrated molecular model of cerebellar long-term depression and long-term potentiation.

Cellular Modeling and Data Analysis

neurofitter-iconErik De Schutter is well known for the detailed model of the Purkinje cell he developed in the past (6). This model replicates the complete morphology and electrophysiology of the neuron (7). We have now developed a new Purkinje cell model that incorporates recent data about voltage-gated channels and use it to investigate the complex spike. We also contribute to the development of models of other neurons in the cerebellum (8, 9).

We have extended our stochastic modeling to the cellular level and using the STEPS simulator (1) we demonstrated that the variability of dendritic calcium spikes, which has been observed experimentally, is caused by stochastic calcium mechanisms (10). This work links stochasticity at the molecular level with cellular properties. More in general we are interested in the importance of neuronal morphology and excitability for function. We showed that the type of excitability a neuron expresses determines its type of network correlation (11, 12), an important correction of the literature on the subject. We analyze experimental data obtained by collaborating labs. In collaboration with Prof. Sinclair we studied branching points in dendrites, found them to be flat and proved that this is a form wiring length optimization (13).

We are extending analysis of morphology to the network level: what are the properties of the forest of dendritic trees? This is a step towards modeling the development of neuronal morphology using environmental clues. To support such modeling we are developing the NeuroMac software (14).

Network Modeling and Data Analysis

network-modellingWe have a strong interest in cerebellar oscillations (15) which we continue to investigate in detailed 2D (16) and 3D network models of cerebellar cortex. These network models comprise simplified models that capture the excitability of the neurons and detailed connectivity based on real anatomy. They are also used to investigate population coding and learning by the cerebellum and its interaction with other brain structures involved in motor control under normal conditions and in disease (17). At present it is difficult to describe such networks in a general way, independent of specific software packages. We have developed a new method to generate model description languages (18) and contribute to the NineML consortium that develops network description languages.

The output of these network simulations are spike trains and we like to compare these to recordings of simple and complex spikes from Purkinje cells in rodents (19) and monkeys, which we analyze using sophisticated statistical methods. This has resulted in the discovery of a relationship between the duration of complex spikes in Purkinje cells and their firing rates (20) and of multiplexed coding (13) by simple spikes (21).

System Level Modeling

A new research project is the development of a distributed model of motor control learning, comprising sensory and motor cortex, cerebellum, basal ganglia and spinal cord. The eventual goal is to have this model control reaching movements in a detailed biomechanical model of the human limb.

References

  1. I. Hepburn, S. Wils, W. Chen and E. De Schutter: STEPS: Efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Systems Biology 6: 36 (2012). 
  2. I. Hepburn, W. Chen and E. De Schutter: Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations. Journal of Chemical Physics 145: 054118 (2016). 
  3. W. Chen and E. De Schutter: Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers. Frontiers in Neuroinformatics 11: 13 (2017).
  4. F. Santamaria, S. Wils, E. De Schutter and G.J. Augustine: Anomalous diffusion in Purkinje cell dendrites caused by dendritic spines. Neuron 52: 635-648 (2006).
  5. G. Antunes and E. De Schutter: A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression.  Journal of Neuroscience: in press (2012). 
  6. E. De Schutter and J.M. Bower: An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice.  Journal of Neurophysiology 71: 375-400 (1994). 
  7. V. Steuber, W. Mittmann, F.E. Hoebeek, R.A. Silver, C.I. De Zeeuw, M. Häusser and E. De Schutter: Cerebellar LTD and pattern recognition by Purkinje cells. Neuron 54: 121–136 (2007). 
  8. S. Solinas, L. Forti, E. Cesana, J. Mapelli and E. De Schutter, E. D’Angelo: Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells. Frontiers in Cellular Neuroscience 1: 2 (2007). 
  9. V. Steuber, N. Schultheiss, A. Silver, E. De Schutter and D. Jaeger: Determinants of synaptic integration and heterogeneity in rebound firing explored with date-driven models of deep cerebellar nuclei cells. Journal of Computational Neuroscience 30: 633-658 (2011). 
  10. H. Anwar*,  I Hepburn*, H. Nedelescu, W. Chen and E. De Schutter: Stochastic calcium mechanisms cause dendritic calcium spike variability. Journal of Neuroscience>: in press (2013). 
  11. S. Hong, S. Ratté, S. Prescott and E. De Schutter:Single neuron firing properties impact correlation-based population coding. Journal of Neuroscience 32:1413–1428 (2012).
  12. 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 (2013).
  13. Y. Kim, R. Sinclair, N. Chindapol, J. A. Kaandorp and E. De Schutter: The geometry of dendritic trees: minimal wiring cost bifurcations are flat. PLoS Computational Biology 8: e1002474 (2012).
  14. B. Torben-Nielsen and  E. De Schutter: Context-aware modeling of neuronal morphologies. Frontiers in Neuroanatomy 8: 92 (2014).  
  15. R. Maex and E. De Schutter: Resonant synchronization in heterogeneous networks of inhibitory neurons. Journal of Neuroscience 23: 10503-10514 (2003). 
  16. F. M S. de Souza and E. De Schutter: Robustness effect of gap junctions between Golgi cells on cerebellar cortex oscillations. Neural Systems & Circuits 1: 7 (2011). 
  17. P. Botta, F. M. S. de Souza, T. Sangrey, E. De Schutter and F. Valenzuela: Alcohol excites cerebellar Golgi cells by inhibiting the Na+/K+-ATPase. Neuropsychopharmacology 35: 1984-1996 (2010). 
  18. I. Raikov and E. De Schutter: The layer-oriented approach to biological modeling languages. PLoS Computational Biology 8: e1002521 (2012). 
  19. S.-L. Shin, F.E. Hoebeek, M. Schonewille, C.I. De Zeeuw, A. Aertsen and E. De Schutter: Regular temporal patterns in cerebellar Purkinje cell simple spike trains. PLoS One 2: e485 (2007).
  20. P. Warnaar, J. Couto, M. Negrello, M. Junker, A. Smilgin, A. Ignashchenkova, M. Giugliano, P. Thier and E. De Schutter: Duration of Purkinje cell complex spikes increases with their firing frequency. Frontiers in Cellular Neuroscience 9: 122.
  21. S. Hong, M. Negrello, M. Junker, A. Smilgin, P. Thier and E. De Schutter: Multiplexed coding by cerebellar Purkinje neurons. eLife 5: e13810 (2016).