NeuGen: A tool for the generation of realistic morphology of cortical neurons and neural networks in 3D

Abstract We introduce the software package NeuGen for the efficient generation of anatomically accurate synthetic neurons and neural networks. NeuGen generates non-identical neurons of morphological classes of the cortex, e.g., pyramidal cells and stellate neurons, and synaptically connected neural networks in 3D. It is based on sets of descriptive and iterative rules which represent the axonal and dendritic geometry of neurons by inter-correlating morphological parameters. The generation algorithm stochastically samples parameter values from distribution functions induced by experimental data. The generator is adequate for the geometric modelling and for the construction of the morphology. The generated neurons can be exported into a 3D graphic format for visualization and into multi-compartment files for simulations with the program NEURON. NeuGen is intended for scientists aiming at simulations of realistic networks in 3D. The software includes a graphical user interface and is available at http://neugen.uni-hd.de .

[1]  T. Sejnowski,et al.  [Letters to nature] , 1996, Nature.

[2]  B. Sakmann,et al.  Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons , 2001, The Journal of physiology.

[3]  Michael L. Hines,et al.  ModelDB - Making models publicly accessible to support computational neuroscience , 2003, Neuroinformatics.

[4]  L. Råde,et al.  Mathematics handbook for science and engineering , 1995 .

[5]  N. Spruston,et al.  Diversity and dynamics of dendritic signaling. , 2000, Science.

[6]  Bartlett W. Mel,et al.  Dendrites: bug or feature? , 2003, Current Opinion in Neurobiology.

[7]  R E Burke,et al.  A parsimonious description of motoneuron dendritic morphology using computer simulation , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  R Kötter,et al.  Morphology, electrophysiology and functional input connectivity of pyramidal neurons characterizes a genuine layer va in the primary somatosensory cortex. , 2006, Cerebral cortex.

[9]  B. Sakmann,et al.  The Excitatory Neuronal Network of Rat Layer 4 Barrel Cortex , 2000, The Journal of Neuroscience.

[10]  W. Rall.  Branching dendritic trees and motoneuron membrane resistivity. , 1959, Experimental neurology.

[11]  B. Sakmann,et al.  ‐Dynamic representation of whisker deflection by synaptic potentials in spiny stellate and pyramidal cells in the barrels and septa of layer 4 rat somatosensory cortex , 2002, The Journal of physiology.

[12]  Arjen van Ooyen,et al.  Competition in Neuronal Morphogenesis and the Development of Nerve Connections , 2002 .

[13]  Kazuhisa Ichikawa,et al.  A modeling environment with three-dimensional morphology, A-Cell-3D, and Ca2+ dynamics in a spine , 2007, Neuroinformatics.

[14]  Henry Markram,et al.  Deriving physical connectivity from neuronal morphology , 2003, Biological Cybernetics.

[15]  Bert Sakmann,et al.  Sub‐ and suprathreshold receptive field properties of pyramidal neurones in layers 5A and 5B of rat somatosensory barrel cortex , 2004, The Journal of physiology.

[16]  Sergei Nirenburg,et al.  Generation , 2004, Machine Translation.

[17]  Giorgio A. Ascoli,et al.  Reconstruction of Brain Networks by Algorithmic Amplification of Morphometry Data , 1999, IWANN.

[18]  B. Sakmann,et al.  Nonlinear anisotropic diffusion filtering of three-dimensional image data from two-photon microscopy. , 2004 .

[19]  Bartlett W. Mel,et al.  Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell , 2003, Neuron.

[20]  G A Ascoli,et al.  Generation, description and storage of dendritic morphology data. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[21]  W. Rall.  Membrane potential transients and membrane time constant of motoneurons. , 1960, Experimental neurology.

[22]  Hanspeter A. Mallot,et al.  Population networks: a large-scale framework for modelling cortical neural networks , 1996, Biological Cybernetics.

[23]  Andreas T. Schaefer,et al.  Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. , 2003, Journal of neurophysiology.

[24]  G. Ascoli.  Computational Neuroanatomy , 2002, Humana Press.

[25]  R. Silver,et al.  Synaptic connections between layer 4 spiny neurone‐ layer 2/3 pyramidal cell pairs in juvenile rat barrel cortex: physiology and anatomy of interlaminar signalling within a cortical column , 2002, The Journal of physiology.

[26]  Jeffrey L. Krichmar,et al.  L-neuron: A modeling tool for the efficient generation and parsimonious description of dendritic morphology , 2000, Neurocomputing.

[27]  F. Plum.  Handbook of Physiology. , 1960 .

[28]  J. Lübke,et al.  Morphometric analysis of the columnar innervation domain of neurons connecting layer 4 and layer 2/3 of juvenile rat barrel cortex. , 2003, Cerebral cortex.

[29]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[30]  Yunheng Ji.  MORPHOLOGY , 1937, A Grammar of Italian Sign Language (LIS).

[31]  J. Lübke,et al.  Columnar Organization of Dendrites and Axons of Single and Synaptically Coupled Excitatory Spiny Neurons in Layer 4 of the Rat Barrel Cortex , 2000, The Journal of Neuroscience.

[32]  B Sakmann,et al.  Functionally Independent Columns of Rat Somatosensory Barrel Cortex Revealed with Voltage-Sensitive Dye Imaging , 2001, The Journal of Neuroscience.

[33]  Bartlett W. Mel.  Synaptic integration in an excitable dendritic tree. , 1993, Journal of neurophysiology.