Computational Neuroanatomy Group: Generation and Description of Dendritic Morphology
Welcome! The Computational Neuroanatomy Group (CNG) is a laboratory dedicated to the investigation of the structure, activity, and function of the nervous system, from single cells to neuronal networks. Established in 1999, the CNG (part of the Krasnow Institute for Advanced Study at George Mason University) has been funded since its inception by an R01 grant from NINDS and NIMH (and, from 2003, NSF), under the Human Brain Project.

One of the main projects of CNG consists of the Generation and Description of Dendritic Morphology. Dendrites have been qualitatively investigated since the times of Golgi and Cajal. Only recently, however, has the use of computer-interfaced microscopes allowed for the acquisition, storage, and sharing of digital reconstructions of dendritic morphology. The opening image of this document represents a detail of two Golgi-stained hippocampal pyramidal cells traced and digitized in the CNG.

Here we provide a few examples of what has been achieved by the CNG in the first four years of Human Brain Project support. Progress includes the development of software for the quantitative analysis of dendritic morphology, the implementation of computational models to simulate neuronal structure, and the synthesis of anatomically accurate, large scale neuronal assemblies in virtual reality.

[Contact]        [Links]        [Bibliography]        [Credits]

L-Measure: click for powerpoint show Analysis: The availability of digitized neuronal reconstructions in principle allows the extraction of any morphological measure from single or multiple cells. We have developed L-Measure, the first freeware software tool to quantitatively analyze dendritic morphology. After 2 years from the first release, L-Measure is used in more than a dozen laboratories in the US and abroad. We recently employed L-Measure to carry out an extensive statistical analysis of publicly available digitized CA3 and CA1 pyramidal neurons. We found surprising differences, not only between the two classes, but also between different reconstructing labs. For a two-minute powerpoint show, click here (turn on the volume!).

turing test
Based on biologically plausible "rules" and biophysical determinants, we have designed stochastic models that can generate realistic virtual neurons. Quantitative morphological analysis indicates that virtual neurons are statistically compatible with the real data that the model parameters are measured from. Here's a "turing test": these 4 pyramidal neurons include 2 simulated and 2 real cells; within each pair, one neuron is from the CA1 and one from the CA3 rat hippocampus. Can you tell the real neurons from the virtual ones? (the answer is at the end of this document...). Apical dendrites are blue, basal are read. Click on the following animations to see the cells "grow" and rotate in 3D. The file available formats are: animated gif, quicktime (mov), mpeg (mp4), and flic (flc). Each format reproduces exactly the same animation for each of the four cells.

Cell1.gif or or cell1.mp4 or cell1.flc

Cell2.gif or or cell2.mp4 or cell2.flc

Cell3.gif or or cell3.mp4 or cell3.flc

Cell4.gif or or cell4.mp4 or cell4.flc

Get the solution of the Turing test (try to guess from the animations first!).

Networks: Virtual neurons can be generated within an appropriate anatomical context if a system level description of the surrounding tissue is included in the model. As a first step towards a real-scale model of the hippocampus, we have traced the mMRI animation
granule and molecular layers of the dentate gyrus from microscopic MRI scans (click on the right MRI image to see through the full stack), and arranged 2000 granule cells within the proper volume and with the correct orientation. Finally, an axon reconstructed from the entorhinal cortex (part of the perforant path, the main afferent to the dentate granule cells), has been added in this virtual reality composition. To see the full animation, select one of these movie formats:

virtualDG1.mpg (mpeg1, medium resolution, should run on all machines)
virtualDG2.mpg (mpeg2, higher resolution, should run on most machines)
virtualDG3.avi (Microsoft Codec 9, higher resolution, should run on Windows machines)
virtualDG4.avi (DVX Codec 5, highest resolution, runs after free codec installation from

In order to simulate anatomically realistic neural networks, axons must be grown as well as dendrites. We have developed a navigation strategy for virtual axons in a voxel substrate. The panels below (zooming in counter-clockwise from top left) are an example of simulated axons stemming from virtual cortical cells and navigating towards the thalamus through a substrate of voxels corresponding to a (real) stained section of the human brain. Each virtual cell is assigned a different color.

voxel navigation

Contact: Giorgio Ascoli,, Tel. +1-703-993-4383


Computational Neuroanatomy Group (follow links for more)
Human Brain Project
Krasnow Institute for Advanced Study
George Mason University
Southampton Archive of Neuronal Morphology (and Cell Viewer)
Gulyas' Collection (CA1 pyramidal cells and interneurons)
ImageJ NeuroMorpho plugin


BOOK: Ascoli G. (Ed.): Computational Neuroanatomy - Principles and Methods (19 chapters, 468 pages, plus CD-ROM). Humana Press, Totowa, NJ (2002).

Relevant Papers (1997-2003):

Ascoli G., Goldin R.: Coordinate systems for dendritic spines: a somatocentric approach. Complexity 2(4):40-48 (1997).

Krichmar J., Ascoli G., Hunter L., Olds J.: A model of cerebellar saccadic motor learning using qualitative reasoning. Lect. Notes Comp. Sci. 1240:134-145 (1997).

Vandersluis J., Cooke J., Ascoli G., Krichmar J., Michaels G., Montgomery M., Symanzyk J., Vitucci B.: Exploratory statistical graphics for an initial motion control experiment. Comp. Sci. Stat. 30:482-487 (1998).

Senft S., Ascoli G.: Reconstruction of brain networks by algorithmic amplification of morphometry data. Lect. Notes Comp. Sci., 1606:25-33 (1999).

Ascoli G.: Progress and perspectives in computational neuroanatomy. Anatom. Rec. 257(6):195-207 (1999).

Symanzik J, Ascoli G., Washington S., Krichmar J.: Visual data mining of brain cells. Comp. Sci. Stat.,  31:445-449 (1999).

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

Washington S., Ascoli G., Krichmar J.: A statistical analysis of dendritic morphology’s effect on neuron electrophysiology of CA3 pyramidal cells. Neurocomputing, 32-33:261-269 (2000).

Ascoli G.: The complex link between neuroanatomy and consciousness. Complexity, 6(1):20-26 (2000).

Nasuto S., Krichmar J., Knape R., Ascoli G.: Relation between neuronal morphology and electrophysiology in the kainate lesion model of Alzheimer's Disease. Neurocomputing, 38-40:1477-1487 (2001).

Scorcioni R., Ascoli G.: Algorithmic extraction of morphological statistics from electronic archives of neuroanatomy. Lect. Notes Comp. Sci., 2084:30-37 (2001).

Ascoli G., Krichmar J., Nasuto S., Senft S.: Generation, description, and storage of dendritic morphology data. Phil. Trans. R. Soc. B, 356(1412):1131-45 (2001).

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

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

Ascoli G.: Neuroanatomical algorithms for dendritic modeling. Network: Comput. Neural Syst. 13:247-260 (2002).

Krichmar J., Nasuto S., Scorcioni R., Washington S., Ascoli G.: Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study. Brain Res., 941:11-28 (2002).

Lazarewicz M., Migliore M., Ascoli G.: A new bursting model of CA3 pyramidal cell physiology suggests multiple locations for spike initiation. Biosystems, 67:129-37 (2002).

Samsonovich A., Ascoli G.: Statistical morphological analysis of hippocampal principal neurons indicates selective repulsion of dendrites from their own cell. J. Neurosci. Res. 71:173-87 (2003).

Gardner D., Toga A., Ascoli G., Beatty J., Brinkley J., Dale A., Fox P., Gardner E., George J., Goddard N., Harris K., Herskovits E., Hines M., Jacobs G., Jacobs R., Jones E., Kennedy D., Kimberg D., Mazziotta J., Miller P., Mori S., Mountain D., Reiss A., Rosen G., Rottenberg D., Shepherd G., Smalheiser N., Smith K., Strachan T., Van Essen D., Williams R., Wong S.: Sharing Data, Carefully. Neuroinformatics, 1:289-295 (2003).

Ascoli G.: Passive dendritic integration heavily affects spiking dynamics of recurrent networks. Neural Networks, 16:657-663 (2003).

Scorcioni R., Lazarewicz M.T., Ascoli G.: Quantitative morphometry of hippocampal pyramidal cells: differences between anatomical classes and reconstructing laboratories. In Press, J. Comp. Neurol. (2004).

Relevant Book Chapters and Peer-reviewed Full-length Proceedings (1997-2003):

Krichmar J., Ascoli G.,  Olds J., Hunter L.: The qualitative reasoning neuron: a new approach to modeling in computational neuroscience. In J.M. Bower (Ed.): Computational Neuroscience: Trends in Research 1998, 609-614, Plenum Press, New York, NY (1998).

Nasuto S., Krichmar J., Scorcioni R., Ascoli G.: Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons. InterJournal Complex Syst. R389:1-6 (2000).

Ascoli G.: Computing the brain and the computing brain. In G. Ascoli (Ed.): Computational Neuroanatomy: Principles and Methods, 3-26, Humana Press, Totowa, NJ (2002).

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

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

Samsonovich A., Ascoli G.: Towards virtual brains. In G. Ascoli (Ed.): Computational Neuroanatomy: Principles and Methods, 425-436, Humana Press, Totowa, NJ (2002).

Turner DA,  Cannon RC, Ascoli GA: Web-based neuronal archives: neuronal morphometric and electrotonic analysis. In R. Kotter (Ed.): Neuroscience Databases – A Practical Guide, 81-98, Elsevier, Amsterdam (2002).

Ascoli G.,  Samsonovich A.: Bayesian morphometry of hippocampal cells suggests same-cell
somatodendritic repulsion. In Dietterich T.G., Becker S. Ghahramani Z. (Eds.): Adv. Neural Proc. Syst. 14:133-139 (2002).

Ascoli G.: Electrotonic effects on spike response model dynamics. IEEE Neural Networks, in press (2003).

Donohue D., Ascoli G.: Models of neuronal outgrowth. In Koslow S.H. and Subramaniam S. (Eds.): Databasing the Brain: From Data to Knowledge, Wiley, New York, NY. In Press (2004).


This worked was supported by grant R01-39600 from NIMH, NINDS, and NSF under the Human Brain Project of the Office of Neuroinformatics.

Current members of the Computational Neuroanatomy Group include:

Giorgio Ascoli, Principal Investigator
Xiaoshen Li, Postdoc
Alexei Samsonovich, Postdoc
Ruggero Scorcioni, Postdoc
Duncan Donohue, PhD Student
Deepak Ropyreddy, PhD Student
John Atkeson, MA Student
Sridevi Polavaram, MA Student
Kerry Brown, Pre-doc Student
David Velasquez, Pre-doc Student
Stephen Senft, Research Associate

In particular, material for this document has been contributed by Alexei Samsonovich (Turing Test), Ruggero Scorcioni (Virtual Hippocampus), and Steve Senft (Thalamocortical Projections).

This work would not be possible without the willingness of all active neuroscientists to generously share their raw data and electronic tools with the community. In particular, we are grateful to Drs. David Amaral, German Barrionuevo, Jean-Marie Bouteiller, Gyuri Buzsaki, Robert Cannon, Brenda Claiborne, Giampaolo D'Alessandro, Attila Gulyas, David Lester, Robert Malenka, Michele Migliore, Nobu Tamamaki, and Dennis Turner

Solution to the Turing Test:

Cell1: Real CA3
Cell2: Virtual CA1
Cell4: Real CA
Cell3: Virtual CA3