Volume 12 Supplement 1

Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access

Computational study of structural changes in neuronal networks during growth: a model of dissociated neocortical cultures

  • Jugoslava Aćimović1Email author,
  • Tuomo Mäki-Marttunen1, 2 and
  • Marja-Leena Linne1
BMC Neuroscience201112(Suppl 1):P203

DOI: 10.1186/1471-2202-12-S1-P203

Published: 18 July 2011

Networks of neurons possess distinct structural organization that constraints generated activity patterns, and consequently, the functions of the system. The emergence of the network structure can be understood by studying the rules that govern growth of neurons and their self-organization into neuronal circuits. We analyze these rules using a computational model of growth developed for dissociated neocortical cultures. Compared to the growth in vivo, the cultures represent simplified two dimensional systems that still possess the intrinsic properties of single neurons although they lack the natural extracellular environment present in vivo. This setup provides a possibility to address in depth the selected mechanisms that affect neuronal growth. The collected structural data (through staining and microscopy) and electrophysiological data (using microelectrode arrays) facilitate validation of computational models. Neuronal growth in dissociated cultures has been examined in several studies in order to access the role of activity in network development [6],[7] or to extract the structural changes during growth from the recorded activity and identify the significant time points in network development [4]. In addition, two simulators of neuronal growth were recently published to aid the development of computational models [3],[9]. Their performance, in context of modeling neocortical cultures, is compared in [1].

The analyzed model consists of two types on neurons, most commonly observed in the neocortical cultures, the pyramidal cells and the nonpyramidal GABAergic cells, placed in a dish-like space with the density of cells corresponding to the experimental values. The phenomenological model that takes into account growth of every neurite is constructed using the description from the literature [3],[8]. It is compared to the model that defines only the overall shape of each neuritic field. We examine the critical time point in network development, i.e. the emergence of fully connected networks [2],[4], which is dependent on the overall growth speed of neurites. The local structural features are accessed using the frequency of motifs in networks [2],[5]. Local connectivity patterns, captured by the motif counts, depend on the shape of neurites and distribution of synaptic contacts along neurites. The goal of this study is to analyze model dynamics through evaluation of the proposed measures. The dependence on model parameters is examined in details, particularly, whether small variations in parameter values significantly affect both measures of network structure. The obtained conclusions are compared to the experimental findings from the literature [4, 5].

Declarations

Acknowledgements

The authors would like to acknowledge the following funding: Academy of Finland project no. 213462 (Center of Excellence in Signal Processing), and TISE graduate school.

Authors’ Affiliations

(1)
Department of Signal Processing, Tampere University of Technology
(2)
Department of Mathematics, Tampere University of Technology

References

  1. Aćimović J, Mäki-Marttunen T, Havela R, Teppola H, Linne ML: Models of neuronal growth in vitro: Comparison of two simulators of growth, CX3D and NETMORPH. EURASIPJournal Bioinf Syst Biol.
  2. Dorogovtsev S, Mendez J: Evolution of Networks: From Biological Nets to the Internet and WWW. 2003, Oxford University Press, NTView ArticleGoogle Scholar
  3. Koene RA, Tijms B, van Hees P, Postma F, de Ridder A, Ramakers GJA, van Pelt J, van Ooyen A: NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies. Neuroinformatics. 2009, 7 (3): 195-210. 10.1007/s12021-009-9052-3.View ArticlePubMedGoogle Scholar
  4. Soriano J, Martinez MR, Tlusty T, Moses E: Development of input connections in neural cultures. Proc Natl Acad Sci U S A. 2008, 105 (37): 13758-13763. 10.1073/pnas.0707492105.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Sporns O: Networks of the brain. 2011, The MIT Press, Cambridge, MassachusettsGoogle Scholar
  6. Tetzlaff C, Okujeni S, Egert U, Wörgötter F, Butz M: Self-Organized Criticality in Developing Neuronal Networks. PLoS Comp Biol. 2010, 6 (12).
  7. van Ooyen A, van Pelt J, Corner MA, Kater SB: Activity-Dependent Neurite Outgrowth: Implications for Network Development and Neuronal Morphology. Modeling Neural Development. 2003, The MIT Press, Cambridge, MassachusettsGoogle Scholar
  8. van Pelt J, Uylings HBM: Natural Variablity in the Geometry of the Dendritic Branching Patterns. Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics. 2005, Taylor&Francis, Boca Raton, Florida, USAGoogle Scholar
  9. Zubler F, Douglas R: A framework for modeling the growth and development of neurons and networks. Frontiers Comp Neurosci. 2009, 3 (25).

Copyright

© Aćimović et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement