Projects

Data

Name L-Neuron: generation and description of dendritic morphology

Description We are specifically interested in the description and generation of dendritic morphology, and in its effect on neuronal electrophysiology.

URL http://www.krasnow.gmu.edu/L-Neuron/home.htm

Category Morphology Show Other Neuronal Modeling Show Other Mathematical software Show Other

Scale Circuit Show Other Cells Show Other

Type Theory Show Other

Species

Person; Role Giorgio Ascoli; PI Show Other

Project Description 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. Our group is assembling these algorithms into a software package, named L-Neuron, for the generation and study of anatomically plausible neuronal analogs. The primary goal of the L-Neuron project is to create virtual neurons that are anatomically indistinguishable from their real counterparts. 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.

Database Virtual Neuromorphology Electronic Database Show Other

Shared Tool(s) L-Neuron Show Other L-Measure Show Other