Abstract
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.
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References
Agmon-Snir, H., Carr, C.E., Rinzel, J.: The role of dendrites in auditory coincidence detection. Nature 393, 268–272 (1998)
Ascoli, G.A.: Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nature Neuroscience Reviews 7, 318–324 (2006)
Ascoli, G.A., Krichmar, J.L.: L-Neuron: a modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing 32-33, 1003–1011 (2000)
Ascoli, G.A., et al.: Generation, description and storage of dendritic morphology. Phil. Trans. R. Soc. Lond. B 356, 1131–1145 (2001)
Ascoli, G.A., et al.: Computer generation and quantitative morphometric analysis of virtual neurons. Anat. Embryol. 204, 283–301 (2001)
Carnevale, N., Hines, M.: The NEURON book. Cambridge University Press, Cambridge (2006)
Consoulas, C., et al.: Behavioral transformations during metamorphosis: remodeling of neural and motor systems. Brain research bulletin 53(5), 571–583 (2000)
Duch, C., Levine, R.B.: Remodeling of membrane properties and dendritic architecture accompanies the postembryonic conversion of a slow into a fast motorneuron. J. NeuroScience 20(18), 6950–6961 (2000)
Eberhard, J.P., Wanner, A., Wittum, G.: NeuGen: a toold for the generation of realistic morphology of cortical neurons and neural networks in 3d. Neurocomputing, XX:in press (2006)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kath, W.L.: Computational Modelling of Dendrites. J. Neurobiol 64, 91–99 (2005)
Koch, C., Segev, I.: The role of single neurons in information processing. Nature Neuroscience 3, 1171–1177 (2000)
Koza, J.: Genetic programming: On the programming of computers by means of Natural Selection. MIT Press, Cambridge (1992)
Krichmar, J.L., et al.: Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study. Brain Res. 941, 11–28 (2002)
Lien, J.-M., Morales, M., Amato, N.M.: Neuron PRM: A Framework for Constructing Cortical Networks. Neurocomputing 54-54, 191–197 (2003)
Lindenmayer, A.: Mathematical models for cellular interactions in development i & ii. Journal of Theoretical Biology 18, 280–315 (1968)
London, M., Häusser, M.: Dendritic computation. Annu. Rev. Neurosci. 25, 5003–5532 (2005)
Mainen, Z.F., Sejnowski, T.J.: Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Prusinkiewicz, P., Lindenmayer, A.: The algorithmic beauty of plants. Springer, Heidelberg (1990)
Scott, E.K., Luo, L.: How do dendrites take their shape? Nature (neuroscience) 4(4), 359–365 (2001)
Segev, I.: Sound grounds for computing dendrites. Nature 393, 207–208 (1998)
Segev, I., London, M.: Untangling dendrites with quantitative models. Science 290, 744–749 (2000)
Stepanyants, A., Chklovskii, D.B.: Neurogeometry and potential synaptic connectivity. Trends in Neurosciences 28(7), 387–394 (2005)
Steuber, V., De Schutter, E., Jaeger, D.: Passive model of neurons in the deep cerebellar nuclei: the effect of reconstruction errors. Neurocomputing 58-60, 563–568 (2004)
Stiefel, K.M., Sejnowski, T.J.: Mapping function onto neuronal morphology. J. Neurophysiol, XX:XX (in press) (2006)
Torben-Nielsen, B., Tuyls, K., Postma, E.O.: Shaping realistic neuronal morphologies: An evolutionary computation method. In: International Joint Conference on Neural Networks (IJCNN2006), Vancouver, Canada (2006)
Torben-Nielsen, B., Tuyls, K., Postma, E.O.: EvOL-Neuron: Neuronal Morphology Generation (submitted)
van Ooyen, A., et al.: The effect of dendritic topology on firing patterns in model neurons. Network: Computation in Neural Systems 13, 311–325 (2002)
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Torben-Nielsen, B., Tuyls, K., Postma, E.O. (2007). On the Neuronal Morphology-Function Relationship: A Synthetic Approach. In: Tuyls, K., Westra, R., Saeys, Y., Nowé, A. (eds) Knowledge Discovery and Emergent Complexity in Bioinformatics. KDECB 2006. Lecture Notes in Computer Science(), vol 4366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71037-0_9
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DOI: https://doi.org/10.1007/978-3-540-71037-0_9
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