University of Hertfordshire

By the same authors

Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Pages355-362
Number of pages8
Volume7552 LNCS
ISBN (Electronic)978-3-642-33269-2
ISBN (Print)978-3-642-33268-5
DOIs
Publication statusPublished - 2012
EventICANN 2012 - Lausanne, Switzerland
Duration: 11 Sep 2012 → …

Conference

ConferenceICANN 2012
CountrySwitzerland
CityLausanne
Period11/09/12 → …

Abstract

This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.

Projects

ID: 1507361