Skip to main content
SpringerLink
Log in

Neuron and Network Modeling

  • Chapter
Book cover Neuroanatomical Tract-Tracing 3

Abstract

Computer technology constitutes a formidable asset in the acquisition, manipulation, analysis, and modeling of neuroanatomical data. Single-cell arborizations can be digitally represented as a large number of connected cylinders. In this form, neuronal structure is amenable to three-dimensional (3D) rendering, extensive quantitative characterization, and computational modeling of biophysics, electrophysiology, outgrowth, network connectivity, and dynamics. This chapter describes the state of the art in neuron and network modeling, with particular emphasis on the methods to acquire, analyze, and synthesize neuroanatomical data. Several commercial and freeware systems are available to reconstruct neuronal morphology in digital format, from a variety of preparations, either directly from the microscope or off-line from captured images. The resulting, increasing amount of digital data (and meta-data) can be archived and publicly distributed to maximize scientific impact. This database enables continuing efforts in modeling dendritic branching of neurons throughout the central nervous system, including cortex, cerebellum, and spinal cord. The experimental acquisition of complete axonal projections from single neurons poses additional challenges, which are only recently being overcome. The combination of dendritic and axonal reconstructions (or models), together with the surface and volumetric representation of the surrounding tissue, allows the computational derivation of synaptic connectivity. Taken together, such models constitute a powerful substrate for the implementation of large-scale, anatomically realistic neural networks. These advances can be instrumental for the cross-scale elucidation of the relationship between structure, activity, and function in the brain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ascoli, G. A., 1999, Progress and perspectives in computational neuroanatomy, Anat. Rec. 257(6):195–207.

    Article  PubMed  CAS  Google Scholar 

  • Ascoli, G. A., 2002a, Neuroanatomical algorithms for dendritic modelling, Network 13(3):247–260.

    PubMed  Google Scholar 

  • Ascoli, G. A., 2002b, Computing the brain and the computing brain, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 3–26.

    Chapter  Google Scholar 

  • Ascoli, G. A., 2003, Passive dendritic integration heavily affects spiking dynamics of recurrent networks, Neural Netw. 16:657–663.

    Article  PubMed  Google Scholar 

  • Ascoli, G. A., and Atkeson, J. C., 2005, Incorporating anatomically realistic cellular-level connectivity in neural network models of the rat hippocampus, Biosystems. 79:173–181.

    Article  PubMed  Google Scholar 

  • Ascoli, G. A., De Schutter, E., and Kennedy, D. N., 2003, An information science infrastructure for neuroscience, Neuroinformatics 1(1):1–2.

    Article  PubMed  Google Scholar 

  • Ascoli, G. A., and Krichmar, J. L., 2000, L-Neuron: a modeling tool for the efficient generation and parsimonious description of dendritic morphology, Neurocomputing 32–33:1003–1011.

    Article  Google Scholar 

  • Ascoli, G. A., Krichmar, J. L., Nasuto, S. J., and Senft, S. L., 2001a, Generation, description, and storage of dendritic morphology data, Philos. Trans. R. Soc. Lond. B Biol. Sci. 356(1412):1131–1145.

    Article  PubMed  CAS  Google Scholar 

  • Ascoli, G. A., Krichmar, J. L., Scorcioni, R., Nasuto, S. J., and Senft, S. L., 2001b, Computer generation and quantitative morphometric analysis of virtual neurons, Anat. Embryol. 204(4):283–301.

    Article  PubMed  CAS  Google Scholar 

  • Bota, M., and Arbib, M. A., 2004, Integrating databases and expert systems for the analysis of brain structures: connections, similarities, and homologies, Neuroinformatics 2(1):19–58.

    Article  PubMed  Google Scholar 

  • Buettner, H. M., 1995, Computer simulation of nerve growth cone filopodial dynamics for visualization and analysis, Cell Motil. Cytoskeleton 32(3):187–204.

    Article  PubMed  CAS  Google Scholar 

  • Burke, R. E., and Marks, W. B., 2002, Some approaches to quantitative dendritic morphology, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 27–48.

    Chapter  Google Scholar 

  • Cannon, R. C., Turner, D. A., Pyapali, G. K., and Wheal, H. V., 1998, An online archive of reconstructed hippocampal neurons, J. Neurosci. Methods 84(1–2):49–54.

    Article  PubMed  CAS  Google Scholar 

  • Carreira-Perpinan, M. A., and Goodhill, G. J., 2002, Development of columnar structures in visual cortex, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 337–358.

    Chapter  Google Scholar 

  • Cherniak, C., Mokhtarzada, Z., and Nodelman, U., 2002, Optimal-wiring models of neuroanatomy, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 71–82.

    Chapter  Google Scholar 

  • Chklovskii, D. B., Schikorski, T., and Stevens, C. F., 2002, Wiring optimization in cortical circuits, Neuron 34(3):341–347.

    Article  PubMed  CAS  Google Scholar 

  • Costa Lda, F., and Manoel, E. T., 2003, A percolation approach to neural morphometry and connectivity, Neuroinformatics 1(1):65–80.

    Article  PubMed  Google Scholar 

  • Costa Lda, F., Barbosa, M. S., Coupez, V., and Stauffer, D., 2003, Morphological Hopfield networks, Brain Mind 4:91–105.

    Article  Google Scholar 

  • Donohue, D. E., and Ascoli, G. A., 2005, Models of neuronal outgrowth, In: Koslow, S. H., and Subramaniam, S. (eds.), Databasing the Brain: From Data to Knowledge, Wiley, New York, NY, pp. 303–323.

    Google Scholar 

  • Donohue, D. E., Scorcioni, R., and Ascoli, G. A., 2002, Generation and description of neuronal morphology using L-Neuron: a case study, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 49–70.

    Chapter  Google Scholar 

  • Eglen, S. J., and Willshaw, D. J., 2002, Influence of cell fate mechanisms upon retinal mosaic formation: a modelling study, Development 129(23):5399–5408.

    Article  PubMed  CAS  Google Scholar 

  • Ewart, D. P., Kuzon, W. M., Jr., Fish, J. S., and McKee, N. H., 1989, Nerve fibre morphometry: a comparison of techniques, J. Neurosci. Methods 29(2):143–150.

    Article  PubMed  CAS  Google Scholar 

  • Gardner, D., Toga, A.W., Ascoli, G. A., Beatty, J. T., Brinkley, J. F., Dale, A. M., Fox, P. T., Gardner, E. P., George, J. S., Goddard, N., Harris, K. M., Herskovits, E. H., Hines, M. L., Jacobs, G. A., Jacobs, R. E., Jones, E. G., Kennedy, D. N., Kimberg, D. Y., Mazziotta, J. C., Miller, P. L., Mori, S., Mountain, D. C., Reiss, A. L., Rosen, G. D., Rottenberg, D. A., Shepherd, G. M., Smalheiser, N. R., Smith, K. P., Strachan, T., Van Essen, D. C., Williams, R. W., and Wong, S. T., 2003, Towards effective and rewarding data sharing, Neuroinformatics 1(3):289–295.

    Article  PubMed  Google Scholar 

  • Glaser, J. R., and Glaser, E. M., 1990, Neuron imaging with Neurolucida—a PC-based system for image combining microscopy, Comput. Med. Imaging Graph. 14(5):307–317.

    Article  PubMed  CAS  Google Scholar 

  • Goodhill, G. J., 1998, Mathematical guidance for axons, Trends Neurosci. 21(6):226–231.

    Article  PubMed  CAS  Google Scholar 

  • Gras, H., and Killmann, F., 1983, NEUREC—a program package for 3D-reconstruction from serial sections using a microcomputer, Comput. Programs Biomed. 17(1–2):145–155.

    Article  PubMed  CAS  Google Scholar 

  • He, W., Hamilton, T. A., Cohen, A. R., Holmes, T. J., Pace, C., Szarowski, D. H., Turner, J. N., and Roysam, B., 2003, Automated three-dimensional tracing of neurons in confocal and brightfield images, Microsc. Microanal. 9(4):296–310.

    Article  PubMed  CAS  Google Scholar 

  • Hilgetag, C. C., and Kaiser, M., 2004, Clustered organisation of cortical connectivity, Neuroinformatics 2:353–360.

    Article  PubMed  Google Scholar 

  • Hines, M. L., and Carnevale, N. T., 2001, NEURON: a tool for neuroscientists, Neuroscientist 7(2):123–135.

    Article  PubMed  CAS  Google Scholar 

  • Izhikevich, E. M., 2004, Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15:1063–1070.

    Article  PubMed  Google Scholar 

  • Jacobs, G. A., and Pittendrigh, C. S., 2002, Predicting emergent properties of neuronal ensembles using a database of individual neurons, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 151–170.

    Chapter  Google Scholar 

  • Kalisman, N., Silberberg, G., and Markram, H., 2003, Deriving physical connectivity from neuronal morphology, Biol. Cybern. 88(3):210–218.

    Article  PubMed  Google Scholar 

  • Kotter, R., Nielsen, P., Dyhrfjeld-Johnsen, J., Sommer, F. T., and Northoff, G., 2002, Multi-level neuron and network modeling in computational neuroanatomy, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, 359–382.

    Chapter  Google Scholar 

  • Krichmar, J. L., and Nasuto, S. J., 2002, The relationship between neuronal shape and neuronal activity, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 105–126.

    Chapter  Google Scholar 

  • Krichmar, J. L., Nasuto, S. J., Scorcioni, R., Washington, S. D., and Ascoli. G. A., 2002, Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study, Brain Res. 941(1–2):11–28.

    Article  PubMed  CAS  Google Scholar 

  • Lazarewicz, M. T., Boer-Iwema, S., and Ascoli, G. A., 2002a, Practical aspects in anatomically accurate simulations of neuronal electrophysiology, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp, 127–148.

    Chapter  Google Scholar 

  • Lazarewicz, M.T., Migliore, M., and Ascoli, G. A., 2002b, A new bursting model of CA3 pyramidal cell physiology suggests multiple locations for spike initiation, Biosystems 67:129–137.

    Article  PubMed  CAS  Google Scholar 

  • Leergaard, T. B., and Bjaalie, J. G., 2002, Architecture of sensory map transformations: axonal tracing in combination with 3-d reconstruction, geometric modeling, and quantitative analyses, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 199–218.

    Chapter  Google Scholar 

  • Lester, D. S., Hanig, J. P., and Pine, P. S., 2002, Quantitative neurotoxicity, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 383–400.

    Chapter  Google Scholar 

  • Mainen, Z. F., and Sejnowski, T. J., 1996, Influence of dendritic structure on firing pattern in model neocortical neurons, Nature 382(6589):363–366.

    Article  PubMed  CAS  Google Scholar 

  • Migliore, M., Morse, T. M., Davison, A. P., Marenco, L., Shepherd, G. M., and Hines, M. L., 2003, Model DB: making models publicly accessible to support computational neuroscience, Neuroinformatics 1(1):135–139.

    Article  PubMed  Google Scholar 

  • Mitchison, G., 1992, Axonal trees and cortical architecture, Trends Neurosci. 15(4):122–126.

    Article  PubMed  CAS  Google Scholar 

  • Mori, S., 2002, Principle and applications of diffusion tensor imaging: a new MRI technique for neuroanatomical studies, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 271–292.

    Chapter  Google Scholar 

  • Rodriguez, A., Ehlenberger, D., Kelliher, K., Einstein, M., Henderson, S. C., Morrison, J. H., Hof, P. R., and Wearne, S. L., 2003, Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images, Methods 30(1):94–105.

    Article  PubMed  CAS  Google Scholar 

  • Samsonovich, A. V., and Ascoli, G. A., 2002, Towards virtual brains, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 425–436.

    Chapter  Google Scholar 

  • Samsonovich, A. V., and Ascoli, G. A., 2003, Statistical morphological analysis of hippocampal principal neurons indicates cell-specific repulsion of dendrites from their own cell, J. Neurosci. Res. 71(2):173–187.

    Article  PubMed  CAS  Google Scholar 

  • Samsonovich, A. V., and Ascoli, G. A., 2005, Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: a hidden Markov model, Hippocampus 15: 166–183.

    Article  PubMed  Google Scholar 

  • Samsonovich, A. V., and Ascoli, G. A., 2005, Algorithmic description of hippocampal granule cell dendritic morphology, Neurocomputing 65–66:253–260.

    Article  Google Scholar 

  • Schaefer, A. T., Larkum, M. E., Sakmann, B., and Roth, A., 2003, Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern, J. Neurophysiol. 89(6):3143–3154.

    Article  PubMed  Google Scholar 

  • Scorcioni, R., and Ascoli, G. A., 2001, Algorithmic extraction of morphological statistics from electronic archives of neuroanatomy, Lect. Notes Comp. Sci. 2084:30–37.

    Google Scholar 

  • Scorcioni, R., and Ascoli, G. A., 2005, Algorithmic reconstruction of complete axonal arborizations in rat hippocampal neurons, Neurocomputing 65–66:15–22.

    Article  Google Scholar 

  • Scorcioni, R., Boutiller, J. M., and Ascoli, G. A., 2002, A real scale model of the dentate gyrus based on single-cell reconstructions and 3D rendering of a brain atlas, Neurocomputing 44–46:629–634.

    Article  Google Scholar 

  • Scorcioni, R., Lazarewicz, M. T., and Ascoli, G. A., 2004, Quantitative morphometry of hippocampal pyramidal cells: differences between anatomical classes and reconstructing laboratories, J. Comp. Neurol. 473(2):177–193.

    Article  PubMed  Google Scholar 

  • Segev, R., and Ben-Jacob, E., 2000, Generic modeling of chemotactic based self-wiring of neural networks, Neural Netw. 13(2):185–199.

    Article  PubMed  CAS  Google Scholar 

  • Senft, S. L., 2002, Axonal navigation through voxel substrates: a strategy for reconstructing brain circuitry, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 245–270.

    Chapter  Google Scholar 

  • Senft, S. L., and Ascoli, G. A., 1999, Reconstruction of brain networks by algorithmic amplification of morphometry data, Lect. Notes Comp. Sci. 1606:25–33.

    Article  Google Scholar 

  • Shetty, A. K., and Turner, D. A., 1999, Aging impairs axonal sprouting response of dentate granule cells following target loss and partial deafferentation, J. Comp. Neurol. 414(2):238–254.

    Article  PubMed  CAS  Google Scholar 

  • Stepanyants, A., Tamas, G., and Chklovskii, D. B., 2004, Class-specific features of neuronal wiring, Neuron. 43(2):251–259.

    Article  PubMed  CAS  Google Scholar 

  • Tamamaki, N., Abe, K., and Nojyo, Y., 1988, Three-dimensional analysis of the whole axonal arbors originating from single CA2 pyramidal neurons in the rat hippocampus with the aid of a computer graphic technique, Brain Res. 452(1–2):255–272.

    Article  PubMed  CAS  Google Scholar 

  • Turner, D. A., Cannon, R. C., and Ascoli, G. A., 2002, Web-based neuronal archives: neuronal morphometric and electrotonic analysis, In: Kotter, R. (ed.), Neuroscience Databases—A Practical Guide, Amsterdam: Elsevier, pp. 81–98.

    Google Scholar 

  • Van Ooyen, A., Duijnhouwer, J., Remme, M.W.H., and Van Pelt, J., 2002, The effect of dendritic topology on firing patterns in model neurons, Network 13:311–325.

    Article  PubMed  Google Scholar 

  • Van Ooyen, A., and Van Pelt, J., 2002, Competition in neuronal morphogenesis and the development of nerve connections, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 219–244.

    Chapter  Google Scholar 

  • Vetter, P., Roth, A., and Hausser, M., 2001, Propagation of action potentials in dendrites depends on dendritic morphology, J. Neurophysiol. 85(2):926–937.

    PubMed  CAS  Google Scholar 

  • Winslow, J. L., Jou, S. F., Wang, S., and Wojtowicz, J. M., 1999, Signals in stochastically generated neurons, J. Comput. Neurosci. 6(1):5–26.

    Article  PubMed  CAS  Google Scholar 

  • Wolf, E., Birinyi, A., and Pomahazi, S., 1995, A fast three-dimensional neuronal tree reconstruction system that uses cubic polynomials to estimate dendritic curvature, J. Neurosci. Methods 63:137–145.

    Article  PubMed  CAS  Google Scholar 

  • Yates, P. A., Holub, A. D., McLaughlin, T., Sejnowski, T. J., and O’Leary, D. D., 2004, Computational modeling of retinotopic map development to define contributions of EphA-ephrinA gradients, axon-axon interactions, and patterned activity, J. Neurobiol. 59(1):95–113.

    Article  PubMed  CAS  Google Scholar 

  • Young, M. P., and Scannell, J.W., 1996, Component-placement optimization in the brain, Trends Neurosci. 19(10):413–415.

    PubMed  CAS  Google Scholar 

  • Zaborszky, L., Csordas, A., Buhl, D., Duque, A., Somogyi, J., and Nadasdy, Z., 2002, Computational anatomical analysis of the basal forebrain corticopetal system, In: Ascoli, G. A. (ed.), Computational Neuroanatomy: Principles and Methods, Totowa, NJ: Humana Press, pp. 171–198.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Krasnow Institute for Advanced Study and Psychology Department, George Mason University, 4400 University Drive, MS2A1, Fairfax, VA, 22030-4444

    Giorgio A. Ascoli & Ruggero Scorcioni

Authors
  1. Giorgio A. Ascoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruggero Scorcioni
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA

    Laszlo Zaborszky

  2. Department of Anatomy, Vrije University Medical Center, Amsterdam, The Netherlands

    Floris G. Wouterlood

  3. Neurosciences Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain

    José Luis Lanciego

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Ascoli, G.A., Scorcioni, R. (2006). Neuron and Network Modeling. In: Zaborszky, L., Wouterlood, F.G., Lanciego, J.L. (eds) Neuroanatomical Tract-Tracing 3. Springer, Boston, MA . https://doi.org/10.1007/0-387-28942-9_19

Download citation

Publish with us

Policies and ethics

Search

Navigation