A new approach to reconstruction models of dendritic branching patterns.

Quantitative models for characterising the detailed branching patterns of dendritic trees aim to explain these patterns either in terms of growth models based on principles of dendritic development or reconstruction models that describe an existing structure by means of a canonical set of elementary properties of dendritic morphology, which when incorporated into an algorithmic procedure will generate samples of dendrites that are statistically indistinguishable in both canonical and emergent features from those of the original sample of real neurons. This article introduces a conceptually new approach to reconstruction modelling based on the single assumption that dendritic segments are built from sequences of units of constant diameter, and that the distribution of the lengths of units of similar diameter is independent of location within a dendritic tree. This assumption in combination with non-parametric methods for estimating univariate and multivariate probability densities leads to an algorithm that significantly reduces the number of basic parameters required to simulate dendritic morphology. It is not necessary to distinguish between stem and terminal segments or to specify daughter branch ratios or dendritic taper. The procedure of sampling probability densities conditioned on local morphological features eliminates the need, for example, to specify daughter branch ratios and dendritic taper since these emerge naturally as a consequence of the conditioning process. Thus several basic parameters of previous reconstruction algorithms become emergent parameters of the new reconstruction process. The new procedure was applied successfully to a sample of 51 interneurons from lamina II/III of the spinal dorsal horn.

[1]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[2]  Giorgio A. Ascoli,et al.  Local Diameter Fully Constrains Dendritic Size in Basal but not Apical Trees of CA1 Pyramidal Neurons , 2005, Journal of Computational Neuroscience.

[3]  G A Ascoli,et al.  Generation, description and storage of dendritic morphology data. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[4]  Jaap van Pelt and Harry B.M. Uylings Natural Variability in the Geometry of Dendritic Branching Patterns , 2005 .

[5]  Jeffrey L. Krichmar,et al.  Computer generation and quantitative morphometric analysis of virtual neurons , 2001, Anatomy and Embryology.

[6]  Olaf Sporns,et al.  Modeling in the neurosciences : from biological systems to neuromimetic robotics , 2005 .

[7]  G A Ascoli,et al.  Progress and perspectives in computational neuroanatomy , 1999, The Anatomical record.

[8]  R E Burke,et al.  A parsimonious description of motoneuron dendritic morphology using computer simulation , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  A. Larkman Dendritic morphology of pyramidal neurones of the visual cortex of the rat: I. Branching patterns , 1991, The Journal of comparative neurology.

[10]  F. G. Worden,et al.  The neurosciences : fourth study program , 1979 .

[11]  D. J. Maxwell,et al.  Myelinated and unmyelinated primary afferent axons form contacts with cholinergic interneurons in the spinal dorsal horn , 2002, Experimental Brain Research.