Rendering images of reconstructed neurons
- To: mathgroup at christensen.cybernetics.net
- Subject: [mg1258] Rendering images of reconstructed neurons
- From: hagai at helix.nih.gov (Hagai Agmon-Snir)
- Date: Mon, 29 May 1995 02:48:50 -0400
- Organization: Mathematical Research Branch, NIDDK
Hi, The following post was sent to some biology newsgroups. However, I thought that some of you would like to play with the package, having an opportunity to see dendritic trees of neurons on their screen. Being an amateur user of Mathematica (I am ashamed to say, because I am an extensive user of it), I'll be happy to get your comments and suggestions about it. You don't have to know anything about neuroscience for getting the graphics. As written somewhere below, if any of you needs explanation about the package and its use, I'll be happy to assist. Hagai Mathematica Package for Morphoelectrotonic Transforms A first version of a Mathematica package for rendering Morphoelectrotonic Transforms (METs) of reconstructed dendritic structures is available by anonymous ftp. This package provides means for reading, transforming and rendering dendrites in various METs. Also available are a sample Mathematica notebook and two public domain morphological files with the appropriate format (we thank Rodney Douglas for making his reconstructed neurons available to the neuroscience public). The Morphoelectrotonic Transform is described in (see abstract below): Zador, A., Agmon-Snir, H., and Segev, I. 1995. The morphoelectrotonic transform: a graphical approach to dendritic function. J. Neuroscience 15: 1669-1682. The files can be reached using the web at ftp://bart.niddk.nih.gov/pub/hagai/MET or using ftp: ftp bart.niddk.nih.gov enter as anonymous, enter your email address as password and cd pub/hagai/MET. All the files are ascii. Any comments are welcome. For any help in customizing the package for specific needs (or writing the code in other computer language), call: Hagai Agmon-Snir Tel: (301) 496-6136 Surface mail: (301) 496-4325 Mathematical Research Branch, NIDDK Fax: (301) 402-0535 BSA 9190 Rockville Pike - Suite 350 Bethesda, MD 20814-3800 USA E-mail: hagai at helix.nih.gov Tony Zador can be reached at zador at salk.edu. Idan Segev can reached at idan at hujivms.huji.ac.il Abstract of Zador et al., 1995: Electrotonic structure of dendrites plays a critical role in neuronal computational and plasticity. In this paper we develop two novel measures of electrotonic structure that describe intraneuronal signaling in dendrites of arbitrary geometry. The log-attenuation L_ij measures the efficacy, and the propagation delay P_ij the speed, of signal transfer between any two points i and j. These measures are additive, in the sense that if j lies between i and k, the total distance L_ik is just the sum of the partial distances: L_ik = L_ij + L_jk, and similarly P_ik = P_ij + P_jk. This property serves as the basis for the morphoelectrotonic transform (MET), a graphical mapping from morphological into electrotonic space. In a MET, either P_ij or L_ij replace anatomical distance as the fundamental unit and so provide direct functional measures of intraneuronal signaling. The analysis holds for arbitrary transient signals, even those generated by nonlinear conductance changes underlying both synaptic and action potentials. Depending on input location and the measure of interest, a single neuron admits many METs, each emphasizing different functional consequences of the dendritic electrotonic structure. Using a single layer 5 cortical pyramidal, we illustrate a collection of METs that lead to a deeper understanding of the electrical behavior of its dendritic tree. We then compare this cortical cell to representative neurons from other brain regions (cortical layer 2/3 pyramidal, region CA1 hippocampal pyramidal, and cerebellar Purkinje). Finally, we apply the MET to electrical signaling in dendritic spines, and extend this analysis to calcium signaling within spines. Our results demonstrate that the MET provides a powerful tool for obtaining a rapid and intuitive grasp of the functional properties of dendritic trees.