On the Neuronal Morphology-Function Relationship: A Synthetic Approach

Recent investigations emphasized the role of dendrites in the information processing and computational capabilities of a single neuron. On a local electro physiological level, it is known which computations can be done in dendrites. However, it is still largely unknown how the complete dendritic morphology contributes to the function of a single neuron. In this study we present a synthetic approach to investigate the relationship between morphology and function. Our approach is implemented in a software tool and an experiment is presented. In the experiment we generate morphologies that approximate the functional properties of the Nucleus Laminaris. We discuss the possibilities and limitations of our synthesized approach.

[1]  Arjen van Ooyen,et al.  The effect of dendritic topology on firing patterns in model neurons , 2002, Network.

[2]  I Segev,et al.  Untangling dendrites with quantitative models. , 2000, Science.

[3]  A. Lindenmayer.  Mathematical models for cellular interactions in development. I. Filaments with one-sided inputs. , 1968, Journal of theoretical biology.

[4]  Michael L. Hines,et al.  The NEURON Book , 2006 .

[5]  Nancy M. Amato,et al.  Neuron PRM: a framework for constructing cortical networks , 2003, Neurocomputing.

[6]  D. Chklovskii,et al.  Neurogeometry and potential synaptic connectivity , 2005, Trends in Neurosciences.

[7]  Erik De Schutter,et al.  Passive models of neurons in the deep cerebellar nuclei: the effect of reconstruction errors , 2004, Neurocomputing.

[8]  Liqun Luo,et al.  How do dendrites take their shape? , 2001, Nature Neuroscience.

[9]  J. Rinzel,et al.  The role of dendrites in auditory coincidence detection , 1998, Nature.

[10]  Jeffrey L. Krichmar,et al.  Computer generation and quantitative morphometric analysis of virtual neurons , 2001, Anatomy and Embryology.

[11]  T. Sejnowski,et al.  The Monetary Transmission Mechanism in the United Kingdom: Pass-Through and Policy Rules. manuscript , 1996 .

[12]  Gabriel Wittum,et al.  NeuGen: A tool for the generation of realistic morphology of cortical neurons and neural networks in 3D , 2006, Neurocomputing.

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Stuart D. Washington,et al.  Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study , 2002, Brain Research.

[15]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[16]  G. Ascoli.  Mobilizing the base of neuroscience data: the case of neuronal morphologies , 2006, Nature Reviews Neuroscience.

[17]  Carsten Duch,et al.  Behavioral transformations during metamorphosis: remodeling of neural and motor systems , 2000, Brain Research Bulletin.

[18]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

[19]  Eric O. Postma,et al.  Shaping Realistic Neuronal Morphologies: An Evolutionary Computation Method , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[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]  A. Lindenmayer.  Mathematical models for cellular interactions in development. II. Simple and branching filaments with two-sided inputs. , 1968, Journal of theoretical biology.

[22]  T. Sejnowski,et al.  Mapping function onto neuronal morphology. , 2007, Journal of neurophysiology.

[23]  W. Kath,et al.  Computational modeling of dendrites. , 2005, Journal of neurobiology.

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

[25]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[26]  C Duch,et al.  Remodeling of Membrane Properties and Dendritic Architecture Accompanies the Postembryonic Conversion of a Slow into a Fast Motoneuron , 2000, The Journal of Neuroscience.

[27]  Christof Koch,et al.  The role of single neurons in information processing , 2000, Nature Neuroscience.

[28]  Idan Segev,et al.  Sound grounds for computing dendrites , 1998, Nature.