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• W2146752648 abstract "In this paper we examine how a neuron’s dendritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that generates all possible dendritic trees with 22 terminal points, and one that creates representative samples of trees with 128 terminal points. Based on these trees, we construct multi-compartmental models. To assess the performance of the resulting neuronal models, we quantify their ability to discriminate learnt and novel input patterns. We find that the dendritic morphology does have a considerable effect on pattern recognition performance and that the neuronal performance is inversely correlated with the mean depth of the dendritic tree. The results also reveal that the asymmetry index of the dendritic tree does not correlate with the performance for the full range of tree morphologies. The performance of neurons with dendritic tapering is best predicted by the mean and variance of the electrotonic distance of their synapses to the soma. All relationships found for passive neuron models also hold, even in more accentuated form, for neurons with active membranes." @default.
• W2146752648 created "2016-06-24" @default.
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• W2146752648 date "2014-11-08" @default.
• W2146752648 modified "2023-09-30" @default.
• W2146752648 title "Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances" @default.
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• W2146752648 doi "https://doi.org/10.1007/s10827-014-0537-1" @default.
• W2146752648 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4350002" @default.
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• W2146752648 hasPublicationYear "2014" @default.
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