L-Neuron Download 

For Linux you can dowload it, here . After downloading L-Neuron, please use the command 'chmod +x LM.exe' to change its file access permission.

For Windows please use this link .

For help and example files click here

For further questions drop me an email: Ruggero Scorcioni

L-Neuron: Generation and Description of Dendritic Morphology

The primary goal of the L-Neuron project is to create virtual neurons that are anatomically indistinguishable from their real counterparts. L-Neuron uses the formalism of the Lyndenmayer systems in the version known as Turtle Graphics. Particularly, the L-Neuron program is based on a modification of Laurens Lapre's "L-Parser". Instead of working with the standard L-systems algorithms, however, L-Neuron implements sets of neuroanatomical rules discovered by several research groups (and in particular, Hillman's, Tamori', and Burke's). These rules are local and recursive.

The fact that the rules are local means that they establish correlations among geometrical parameters (e.g. a dendritic branch's diameter and taper) independent of their overall position in the tree. The term recursive refers to the fact that, as a branch grows, it stems other branches that follow the same rules. Therefore the same simple algorithm can be reiterated many times as the dendritic tree develops more and more bifurcations (as shown in this figure, representing the Hillman rules). The L-Neuron algorithms read in experimental data to generate virtual structures. The experimental data are in the form of statistical distributions (for example, bifurcation angles in Purkinje cells can be represented with a Gaussian distribution, with a certain average and standard deviation). L-Neuron samples the values of the parameters within these statistical distributions in a stochastic (random) fashion during dendritic growth. Therefore, with the same set of parameter distributions, the program can generate an unlimited number of virtual neurons.

Because L-Neuron implements a stochastic and statistical algorithm, it achieves a form of morphological data compression and amplification. Compression because hundreds of experimental neuronal tracings within a certain morphological class can be described with a handful of statistical distributions, instead of with the classical compartmental description (thousands of lines per neuron). Amplification because, from those hundred real neurons, one can then generate thousands of virtual counterparts.

Note that different sets of statistical distributions correspond to different morphological families. By varying these values, the same algorithm can describe neurons as diverse as pyramidal, granule, Purkinje, or stellate cells. In this screen shot of the L-Neuron viewer, a pyramidal cell was generated with experimental values reported by Hillman. Green segments are basal dendrites, blue segments are apical dendrites, and the soma is drawn in red.

L-Neuron can output its virtual structures in a variety of formats, including virtual reality, graphical files, and the standard neuroanatomical coordinates compatible with neurophysiological simulators such as GENESIS and Neuron.
 

The L-Neuron Project is supported by the Krasnow Institute for Advanced Study and by Human Brain Project grant R01-NS39600-01 awarded to Giorgio Ascoli by the National Institute of Neurological Disorders and Stroke (NIH).