University of Hertfordshire

From the same journal

By the same authors


View graph of relations
Original languageEnglish
Pages (from-to)221-234
JournalJournal of Computational Neuroscience
Early online date8 Nov 2014
Publication statusPublished - 1 Apr 2015


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


This is an Open Access article published under the Creative Commons Attribution license CC BY 4.0 which allows users to read, copy, distribute and make derivative works, as long as the author of the original work is cited

ID: 1372942