Abstract
This paper demonstrates a method to analyse connections in biological neural networks. The signal flow from one neuron to another is established by an axon which carries the output of a neuron, a synapse which is divided into presynapse and postsynapse, and a dendrite which collects numerous signals from a large number of synapses and delivers them to the destination neuron. The postsynapse including its connection to the dendrite is denoted as a dendritic spine. Due to the observation that shape and size of dendritic spines as well as their distribution along a dendrite vary with behavioral experience and age, a relation between learning processes and the morphology of spines can be conjectured. This can be verified only by statistical analysis. Therefore, a pattern recognition system is required which automatically clusters the set of spines into classes in an unsupervised learning process and selects the features to discriminate them. Our approach is based on the idea that the skeletons of the spines including radius information will constitute all significant information.
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© 1995 Springer-Verlag Berlin Heidelberg
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Watzel, R., Braun, K., Hess, A., Scheich, H., Zuschratter, W. (1995). Detection of Dendritic Spines in 3-Dimensional Images. In: Sagerer, G., Posch, S., Kummert, F. (eds) Mustererkennung 1995. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79980-8_19
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DOI: https://doi.org/10.1007/978-3-642-79980-8_19
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