GENERATION AND DESCRIPTION OF DENDRITIC MORPHOLOGY

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DESCRIPTION: (Applicant's Abstract) Despite a general agreement among neuroscientists that dendritic morphology plays an important role in shaping cellular physiology and network connectivity, computational tools for detailed neuromorphological modeling are so far lacking. Such a gap is particularly surprising considering the vast amount of experimental data on the three-dimensional shape of many neuronal classes available in the literature, and the increasingly powerful sophistication of computer graphics and virtual reality. This research project aims at filling this gap. Cajal envisioned neuronal shape as determined by a finite number of intrinsic phenomena, modulated by the extrinsic effect of the environment. Based on this notion, several local rules correlating morphological parameters (e.g. branch diameter and length) have proved to be powerful and parsimonious descriptors of specific aspects of dendritic topology. We are using these successful correlations, together with global geometrical constraints, to implement descriptive algorithms for dendritic morphology. These algorithms will be assembled into a software package, named L-Neuron, for the generation and study of anatomically plausible neuronal analogs. Our implementation is based on L-system, a well-known mathematical formalism particularly suitable to describe branching and recursive structures, and extensively developed in computer graphics. L-Neuron will use experimental distributions of parameters from real-cell anatomical data to generate virtual neurons of various morphological classes. Within each class, the statistically constrained stochastic implementation of the algorithm will produce multiple, non-identical neurons. The generation of sets of virtual neurons is biologically relevant because it discriminates between important morphological parameters and emergent byproducts, which represent redundancies. If the algorithm actually produces accurate and realistic structures, it must contain all the required information and thus completely describes the original morphological family. If there are residual discrepancies between virtual and real neurons, their analysis may lead to the discovery of new geometric constraints and quantitative correlations between dendritic parameters. Generating complete models of dendritic geometry in virtual reality thus stimulates the development of analytical strategies to test whether the virtual neurons are morphologically equivalent to the real ones. L-Neuron will output neuronal structures into various formats, including virtual reality, standard graphic, and anatomical files, also used by compartmental modeling programs such as GENESIS. This variety of options will allow the display, dynamical rendering and quantitative analysis of data as well as their efficient exchange among research groups. The limitation of L-Neuron consists in being oriented toward single-cell analysis, thus making it less suitable for studying the effect of neuronal morphology on network connectivity. However, the simplicity of this system also represents an important advantage because it allows the analysis of the influence of specific intrinsic and extrinsic determinants on neuronal shape, and consequently on neuronal electrophysiology. We believe that this package, portable to all major platforms and freely distributed, will further neuroanatomy, computational modeling, and scientific education.