Computational Neuroanatomy: Principles and Methods by Giorgio A. Ascoli

By Giorgio A. Ascoli

In Computational Neuroanatomy: rules and strategies, the path-breaking investigators who based the sector assessment the foundations and key concepts on hand to start the production of anatomically exact and whole types of the mind. Combining the immense, data-rich box of anatomy with the computational strength of novel undefined, software program, and special effects, those pioneering investigators lead the reader from the subcellular info of dendritic branching and firing to system-level assemblies and versions. A better half CD-ROM contains software program utilized in computational neuroanatomy, pattern facts documents animations of the consequences said within the booklet, high-resolution colour illustrations, and hyperlinks to important web pages.

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Toga AW, Mazziotta JC, Frackowiak RSJ. ) Brain Mapping: The Trilogy, 3 Vol. Set. Academic Press, San Diego, CA, 2000. 22 Ascoli 68. Paydar S, Doan CA, Jacobs GA. Neural mapping of direction and frequency in the cricket cercal sensory system. J Neurosci 1999; 19:1771–1781. 69. Jacobs GA, Theunissen FE. Extraction of sensory parameters from a neural map by primary sensory interneurons. J Neurosci 2000; 20:2934–2943. 70. Zaborszky L, Duque A. Local synaptic connections of basal forebrain neurons. Behav Brain Res 2000; 115:143–158.

These discrete cylinders, plus information that identifies their positions within the dendritic tree, make up the usual computer data files. The position coding systems often vary between data sources, as does the presence or absence of information about the 3D location of each cylinder. 2. TWO-DIMENSIONAL ANALYSIS OF DENDRITES IN ISOLATED NEURONS Quantitative analysis of neuronal dendrites began before the age of computers with the work of Sholl (11), who plotted dendritic branching patterns of Golgi-stained cortical neurons in terms of distance from the soma.

Ascoli GA, Krichmar JL. L-Neuron: a modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing 2000; 32–33:1003–1011. 40. Carriquiry AL, Ireland WP, Kliemann W, Uemura E. Statistical evaluation of dendritic growth models. Bull Math Biol 1991; 53:579–589. 41. Li GH, Qin CD. A model for neurite growth and neuronal morphogenesis. Math Biosci 1996; 132:97–110. 42. Van Pelt J, Uylings HBM. Natural variability in the geometry of dendritic branching patterns.

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