Abstract
Neurons innervate space by their axonal and dendritic arborizations. Synapses can form when axons and dendrites are in close proximity. The geometry of neurons and their numerical densities in space are thus primary factors in the formation of synaptic connectivity of neuronal networks. A simulator of neuronal morphology based on principles of neural development (NETMORPH) has been used to show that realistic network connectivities emerge from realistic neuronal morphologies and simple proximity-based synapse formation rules.
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References
Aćimović J, Mäki-Marttunen T, Havela R, Teppola H, Linne M-L (2011) Modeling of neuronal growth in vitro: comparison of simulation tools NETMORPH and CX3D. EURASIP. J Bioinform Syst Biol 2011, 616382
Ascoli G, Krichmar J (2002) L-neuron: a modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing 32–33:1003–1011
Ascoli GA (2006) Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nat Rev Neurosci 7:318–324
Ascoli GA, Donohue DE, Halavi M (2007) NeuroMorpho.Org: a central resource for neuronal morphologies. J Neurosci 27(35):9247–9251
Burke RE, Marks WB, Ulfhake B (1992) A parsimonious description of motoneuron dendritic morphology using computer simulation. J Neurosci 12:2403–2416
Cuntz H, Forstner F, Borst A, Häusser M (2010) One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Comput Biol 6(8):e1000877
Costa LF, Coelho RC (2005) Growth driven percolations: the dynamics of connectivity in neuronal systems. Eur Phys J B 47:571–581
Costa Lda F, Coelho RC (2008) Growth-driven percolations: the dynamics of community formation in neuronal systems. arXiv:q-bio/0411009v1;[q-bio.NC]
Costa LF, Manoel ETM, Faucereau F, Chelly J, Van Pelt J, Ramakers GJA (2002) A shape analysis framework for neuromorphometry. Network 13:283–310
Eberhard JP, Wanner A, Wittum G (2006) NeuGen: a tool for the generation of realistic morphology of cortical neurons and neuronal networks in 3D. Neurocomputing 70(1–3):327–342
Feldmeyer D, Lübke J, Silver RA, Sakmann B (2002) Synaptic connections between layer 4 spiny neurone- layer 2/3 pyramidal cell pairs in juvenile rat barrel cortex: physiology and anatomy of interlaminar signalling within a cortical column. J Physiol 538:803–822
Gillette T, Brown K, Ascoli G (2001) The DIADEM Metric: comparing multiple reconstructions of the same neuron. Neuroinformatics 9:233–245
Gleeson P, Steuber V, Silver RA (2007) neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54(2):219–235
Hellwig B (2000) A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biol Cybern 82:111–121
Hill SL, Wang Y, Riachi I, Schurmann F, Markram H (2012) Statistical connectivity provides a sufficeint foundations for specific functional conenctivity in neocortical neural circuits. Proc Natl Acad Sci USA 109(42):E2885–E2894
Kim Y, Sinclair R, Chindapol N, Kaandorp JA, De Schutter E (2012) Geometric theory predicts bifurcations in minimal wiring cost trees in biology are flat. PLoS Comput Biol 8(4):e1002474
Koene RA, Tijms B, van Hees P, Postma F, de Ridder S, Ramakers G, van Pelt J, van Ooyen A (2009) NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics 7:195–210
Lamoureux P, Buxbaum RE, Heidemann SR (1998) Axonal outgrowth of cultured neurons is not limited by growth cone competition. J Cell Sci 111:3245–3252
Le Bé JV, Silberberg G, Wang Y, Markram H (2007) Morphological, electrophysiological, and synaptic properties of corticocallosal pyramidal cells in the neonatal rat neocortex. Cereb Cortex 17:2204–2213
Luczak A (2006) Spatial embedding of neuronal trees modeled by diffusive growth. J Neurosci Methods 157:132–141
Mäki-Marttunen T, Aćimović J, Nykter M, Kesseli J, Ruohonen K, Yli-Harja O, Linne M-L (2011) Information diversity in structure and dynamics of simulated neuronal networks. Front Comput Neurosci
Perin R, Berger TK, Markram H (2011) A synaptic organization principle for cortical neuronal groups. Proc Natl Acad Sci USA 108(13):5419–5424
Peters A (1979) Thalamic input to the cerebral cortex. Trends Neurosci 2:1183–1185
Rall W (1959) Branching dendritic trees and motoneuron membrane resistivity. Exp Neurol 1:491–527
Senft S, Ascoli G (1999) Reconstruction of brain networks by algorithmic amplification of morphometry data. Lect Notes Comp Sci 1606:25–33
Samsonovich A, Ascoli G (2007) Computational models of dendritic morphology: From parsimonious description to biological insight. In: Laubichler MG, Müller G (eds) Modeling biology, structures, behaviors, evolution. MIT Press, Cambridge, MA, pp 91–113
Song S, Sjöström PJ, Reigl M, Nelson S, Chklovskii DB (2005) Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol 3(3):e68
Torben-Nielsen B, Vanderlooy S, Postma EO (2008a) Non-parametric algorithmic generation of neuronal morphologies. Neuroinformatics 6:257–277
Torben-Nielsen B, Tuyls K, Postma EO (2008b) EvOL-Neuron: virtual neuron generation. Neurocomputing 71(4–6):963–972
Uylings H, Smit G (1975) Three dimensional branching structure of pyramidal cell dendrites. Brain Res 87:55–60
Uylings HBM, van Pelt J, Parnavelas JG, Ruiz-Marcos A (1994) Geometrical and topological characteristics in the dendritic development of cortical pyramidal and nonpyramidal neurons. In: van Pelt J, Corner MA, Uylings HBM, Lopes da Silva FH (eds) Progress in brain research (Vol 102), The self-organizing brain: from growth cones to functional networks. Elsevier, Amsterdam, pp 109–123
Uylings HBM, van Pelt J (2002) Measures for quantifying dendritic arborizations. Network 13:397–414
Van Ooyen A (2011) Using theoretical models to analyse neural development. Nat Rev Neurosci 12:311–326
Van Ooyen A, Graham BP, Ramakers GJA (2001) Competition for tubulin between growing neurites during development. Neurocomputing 38–40:73–78
Van Pelt J, Uylings HBM (2002) Branching rates and growth functions in the outgrowth of dendritic branching patterns. Network 13:261–281
Van Pelt J, Uylings HBM (2003) Growth functions in dendritic outgrowth. Brain and Mind 4:51–65
Van Pelt J, Uylings HBM (2005) Natural variability in the geometry of dendritic branching patterns. In: Reeke GN, Poznanski RR, Lindsay KA, Rosenberg JR, Sporns O (eds) Modeling in the neurosciences: from biological systems to neuromimetic robotics. CRC Press, Boca Raton, pp 89–115
Van Pelt J, Uylings HBM (2007) Modeling neuronal growth and shape. In: Laubichler MD, Müller GB (eds) Modeling biology – structures, behaviors, evolution. MIT Press, Cambridge, pp 195–215
Van Pelt J, Uylings HBM (2012) The flatness of bifurcations in 3D dendritic trees: an optimal design. Front Comput Neurosci 5:54
Van Pelt J, Van Ooyen A, Uylings HBM (2001) Modeling dendritic geometry and the development of nerve connections. In: de Schutter E, Cannon (CD-ROM) RC (eds) Computational neuroscience: realistic modeling for experimentalist. CRC Press, Boca Raton, pp 179–208
Van Pelt J, Carnell A, de Ridder S, Mansvelder HD, van Ooyen A (2010) An algorithm for finding candidate synaptic sites in computer generated networks of neurons with realistic morphologies. Front Comput Neurosci 4:148
Zubler F, Douglas RA (2009) Framework for modeling the growth and development of neurons and networks. Front Comput Neurosci 3:25
Acknowledgments
This work was supported by grants from the Netherlands Organization for Scientific Research (NWO) through the Program Computational Life Sciences (grant number 635.100.005), the MC-RTN project NEURoVERS-it (grant number 019247) of the European Union, and the BIO-ICT project SECO (grant number 216593) of the Seventh Framework Programme of the European Union.
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van Pelt, J., Uylings, H.B.M., van Ooyen, A. (2014). Neuronal Arborizations, Spatial Innervation, and Emergent Network Connectivity. In: Cuntz, H., Remme, M., Torben-Nielsen, B. (eds) The Computing Dendrite. Springer Series in Computational Neuroscience, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8094-5_4
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DOI: https://doi.org/10.1007/978-1-4614-8094-5_4
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