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A4: Structure formation and structural plasticity of cortical networks

A4: Structure formation and structural plasticity of cortical networks

Stefan RotterB, Marc-Oliver GewaltigD, Gerhard SchneiderN, and Markus DiesmannQ


B
= Bernstein Center for Computational Neuroscience
D = Honda Research Institute Europe GmbH
N = Computing Center
Q = Riken Brain Science Institute


Scientific background

The cortical network experiences continuous reorganization throughout life. The most dramatic structural changes occur during development, but also in the mature brain neurons are continuously born and connections between neurons change due to reshaping of dendritic spines, the preferred location of synapses. The normal fate of a brain includes the programmed death of nerve cells, and dramatic network decay may be induced by drugs or diseases (e.g. Alzheimer, Parkinson). Artificially induced structural plasticity occurs in cultured brain tissue, and when neurons or technical interfaces are implanted into an intact brain. Biomedical research has a great interest in understanding and eventually controlling such structural changes. Numerical simulations may play an important role here, since they allow going beyond experiments, but no currently available tool allows including structural plasticity into neuronal network simulations. The new software required for research in this project will be developed in the framework of NEST, an environment for the simulation of biological neuronal networks.


Objectives

The objective is to develop the technology for simulations of neuronal networks while they undergo structural reorganization. This will allow us, for the first time, to address questions involving normal or pathological changes of network structure (e.g. during development, aging, or repair) that are currently not accessible for theoretical research, due to their computational complexity. We will use our new tool to (a) explore dynamic rewiring in cortical networks; (b) characterize the activity dynamics of networks while they undergo structural reorganization; (c) examine the operation of modulatory systems that guide, control, or terminate structural changes; and (d) study the interplay between activity-dependent structural changes and activity dynamics.


List of project-related publications

  1. Guerrero R., Morrison A., Diesmann M., Pearce T.C. (2006) Programmable logic construction kits for hyper real-time neuronal modeling. Neural Comput. 18(11): 2651–2679.
  2. Morrison A., Straube S., Plesser H.E., Diesmann M. (2007) Exact subthreshold integration with continuous spike times in discrete time neural network simulations. Neural Computation 19: 47–79.
  3. Plesser H.E., Eppler J.M., Morrison A., Diesmann M., Gewaltig M.-O. (2007) Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers. Euro-Par 2007: Parallel Processing 4641:672–681. Kermarrec A.-M., Bouge L., Priol T. (eds.), Lecture Notes in Computer Science, Springer-Verlag, Berlin.
  4. Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower J. M.,Diesmann M., Morrison A., Goodman P.H., Harris Jr. F.C., Zirpe M., Natschläger T., Pecevski D., Ermentrout B., Djurfeldt M., Lansner A., Rochel O., Vieville T., Muller E., Davison A.P., El Boustani S., Destexhe A. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience: DOI 10.1007/s10827-007-0038-6
  5. Helias M., Rotter S., Gewaltig M.-O., Diesmann M. (2008) Structural plasticity controlled by calcium based correlation detection. Front. Comput. Neurosci. 2:7, 1–21.
  6. Eppler J.M., Helias M., Muller E., Diesmann M., Gewaltig M. (2008) PyNEST: A convenient interface to the NEST simulator. Front. Neuroinform. 2, 12. doi:10.3389/neuro.11.012.2008
  7. Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Kotaleski JH, Ekeberg O (2010) Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework. Neuroinformatics DOI 10.1007/s12021-010-9064-z

 

Book chapters:

  1. Morrison A, Diesmann M (2008) Maintaining Causality in Discrete Time Neuronal Network Simulations In: Lectures in supercomputational neuroscience: dynamics in complex brain networks. Chap. IV.10. P. Beim Graben, C. Zhou, M. Thiel, and J. Kurths eds.

 

Abstracts:

  1. Morrison A., Straube S., Plesser H.E., Diesmann M. (2006) Precise and efficient discrete time neural network simulation. In Fifteenth Computational Neuroscience Meeting CNS 2006, Edinburgh, UK, S51.
  2. Eppler J.M., Morrison M., Diesmann M., Plesser H.E., Gewaltig M.-O. (2006) Parallel and Distributed Simulation of Large Biological Neural Networks with NEST. Fifteenth Computational Neuroscience Meeting CNS*2006, Edinburgh, UK, S48.
  3. Diesmann M., Gewaltig M.-O. (2006) Exploring large-scale models of neural systems with the Neural Simulation Tool NEST. Fifteenth Computational Neuroscience Meeting CNS 2006, Edinburgh, UK, S49.
  4. Helias M., Rotter S., Gewaltig M.-O., Diesmann M. (2007) A model for correlation detection based on Ca2+ concentration in spines. 7th Göttingen Meeting of the German Neuroscience Society. Neuroforum XIII, supplement, T37-9A.
  5. Helias M., Rotter S., Gewaltig M.-O., Diesmann M. (2007) A model for correlation detection based on Ca2+ concentration in spines. Sixteenth Computational Neuroscience Meeting CNS*2007, Toronto, Canada.
  6. Helias M., Rotter S., Gewaltig M.-O., Diesmann M. (2008) A biologically realistic model of correlation based structural plasticity. Neuroscience 2008, the Society for Neuroscience's 38th annual meeting, Washington DC, November 2008. Poster no. 326.
  7. Deger M., Helias M., Diesmann M., Rotter S. (2008) Structural plasticity in recurrent cortical networks. 4th Bernstein Symposium, Munich, October 2008.
  8. Helias, M., Rotter, S., Gewaltig, M.O. & Diesmann, M. (2009) Structural plasticity controlled by calcium based correlation detection. Status Seminar and Meeting of the Scientific Advisory Board 2009 of the Helmholtz Alliance on Systems Biology, Oct 26-27, Heidelberg, Germany
  9. Eppler J.M., Helias M., Muller E., Diesmann M. & Gewaltig M.-O. (2009). Convenient simulation of spiking neural networks with NEST 2. Frontiers in Computational Neuroscience. Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.103
  10. M. Deger, M. Helias, M. Diesmann, S. Rotter (2009) Structural plasticity in recurrent cortical networks, T26-13B, Meeting of the German Neuroscience Society, March 25-29 2009, Göttingen
  11. Helias, M., Rotter, S., Gewaltig, M.-O., Diesmann, M. (2009) Self-sustained cell assemblies in structurally plastic networks, T26-5B, Meeting of the German Neuroscience Society 2009, March 25-29, Göttingen
  12. [Eppler J.M., Kupper R., Plesser H.E. and Diesmann M. (2009) A testsuite for a neural simulation engine. In Frontiers in Neuroinformatics. Conference Abstract: 2nd INCF Congress of Neuroinformatics, Plzen, 2009. International Neuroinformatics Coordinating Facility. DOI 10.3389/conf.neuro.11.2009.08.042.
  13. Vlachos A., Bas Orth C., Winkels R., Helias M., Jedlicka P., Roeper J., Schneider G., Deller T. (2009) Time-lapse imaging of mouse granule cells reveals new rules for denervation-induced changes in spine density. Neuroscience 2009, the Society for Neuroscience's 39th annual meeting, Chicago, October 2009. Poster no. 620.18/E19.

 

Talks:

  1. Helias M. (2007). Structural plasticity: A model for correlation detection in maturing synapses. Neurex Plasticity Meeting in Basel, February 2007.
  2. Helias M. (2007) A model for correlation detection in synapses based on calcium concentration. 3rd Bernstein Symposium Göttingen, September 2007.
  3. Helias M. (2008) From correlation detection towards structural plasticity in cortical networks. Sloan-Swartz Centers for Theoretical Neurobiology, Annual Meeting 2008, Princeton, USA.
  4. Helias M. (2008) Structural plasticity controlled by calcium based correlation detection. FACETS Plasticity Meeting 2008, Lausanne, CH.
  5. Helias M. (2008) Structural plasticity controlled by calcium based correlation detection. Laboratory of Neurophysics and Physiology, CNRS – Université René Descartes, Paris, France.
  6. Helias M. (2009) Structural plasticity controlled by calcium based correlation detection. Laboratory of Computational Neuroscience, EPFL Lausanne, Switzerland.
  7. Helias M., Rotter S., M.-O. Gewaltig, Diesmann M. (2008) A calcium based learning rule for structural plasticity, Workshop on Random, Growing, and Infinite Networks, Blaubeuren, Germany
  8. Helias M. (2009) Structural plasticity controlled by calcium based correlation detection. Department of Clinical Neuroanatomy. Goethe-University Frankfurt, Germany
     
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