EvOL-NEURON: Neuronal morphology generation

Ben Torben-Nielsen; Karl Tuyls; Eric Postma

doi:10.1016/j.neucom.2007.02.016

EvOL-NEURON: Neuronal morphology generation

Ben Torben-Nielsen^*, Karl Tuyls, Eric Postma

^*Corresponding author for this work

Robots, Agents, Interaction

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Virtual neurons are essential in computational neuroscience to study the relation between neuronal form and function. One way of obtaining virtual neurons is by algorithmic generation from scratch. However, a main disadvantage of current available generation methods is that they impose a priori limitations on the outcomes of the algorithms. We present a new tool, EvOL-NEURON, that overcomes this problem by putting a posteriori constraints on generated virtual neurons. We present a proof of principle and show that our method is particularly suited to investigate the neuronal form-function relation. (c) 2007 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	963-972
Number of pages	10
Journal	Neurocomputing
Volume	71
Issue number	4-6
DOIs	https://doi.org/10.1016/j.neucom.2007.02.016
Publication status	Published - Jan 2008

Keywords

virtual neuron
neuronal morphology
computational neuroanatomy
DENDRITIC MORPHOLOGY
PARSIMONIOUS DESCRIPTION
HIPPOCAMPAL-NEURONS
ALPHA-MOTONEURONS
PYRAMIDAL NEURONS
RECONSTRUCTION
NETWORKS
PATTERNS
MODELS
TOOL

Access to Document

10.1016/j.neucom.2007.02.016

Cite this

@article{d8537c30aa664cbba33b7e9a88f16cb4,

title = "EvOL-NEURON: Neuronal morphology generation",

abstract = "Virtual neurons are essential in computational neuroscience to study the relation between neuronal form and function. One way of obtaining virtual neurons is by algorithmic generation from scratch. However, a main disadvantage of current available generation methods is that they impose a priori limitations on the outcomes of the algorithms. We present a new tool, EvOL-NEURON, that overcomes this problem by putting a posteriori constraints on generated virtual neurons. We present a proof of principle and show that our method is particularly suited to investigate the neuronal form-function relation. (c) 2007 Elsevier B.V. All rights reserved.",

keywords = "virtual neuron, neuronal morphology, computational neuroanatomy, DENDRITIC MORPHOLOGY, PARSIMONIOUS DESCRIPTION, HIPPOCAMPAL-NEURONS, ALPHA-MOTONEURONS, PYRAMIDAL NEURONS, RECONSTRUCTION, NETWORKS, PATTERNS, MODELS, TOOL",

author = "Ben Torben-Nielsen and Karl Tuyls and Eric Postma",

year = "2008",

month = jan,

doi = "10.1016/j.neucom.2007.02.016",

language = "English",

volume = "71",

pages = "963--972",

journal = "Neurocomputing",

issn = "0925-2312",

publisher = "Elsevier Science",

number = "4-6",

}

TY - JOUR

T1 - EvOL-NEURON

T2 - Neuronal morphology generation

AU - Torben-Nielsen, Ben

AU - Tuyls, Karl

AU - Postma, Eric

PY - 2008/1

Y1 - 2008/1

N2 - Virtual neurons are essential in computational neuroscience to study the relation between neuronal form and function. One way of obtaining virtual neurons is by algorithmic generation from scratch. However, a main disadvantage of current available generation methods is that they impose a priori limitations on the outcomes of the algorithms. We present a new tool, EvOL-NEURON, that overcomes this problem by putting a posteriori constraints on generated virtual neurons. We present a proof of principle and show that our method is particularly suited to investigate the neuronal form-function relation. (c) 2007 Elsevier B.V. All rights reserved.

AB - Virtual neurons are essential in computational neuroscience to study the relation between neuronal form and function. One way of obtaining virtual neurons is by algorithmic generation from scratch. However, a main disadvantage of current available generation methods is that they impose a priori limitations on the outcomes of the algorithms. We present a new tool, EvOL-NEURON, that overcomes this problem by putting a posteriori constraints on generated virtual neurons. We present a proof of principle and show that our method is particularly suited to investigate the neuronal form-function relation. (c) 2007 Elsevier B.V. All rights reserved.

KW - virtual neuron

KW - neuronal morphology

KW - computational neuroanatomy

KW - DENDRITIC MORPHOLOGY

KW - PARSIMONIOUS DESCRIPTION

KW - HIPPOCAMPAL-NEURONS

KW - ALPHA-MOTONEURONS

KW - PYRAMIDAL NEURONS

KW - RECONSTRUCTION

KW - NETWORKS

KW - PATTERNS

KW - MODELS

KW - TOOL

U2 - 10.1016/j.neucom.2007.02.016

DO - 10.1016/j.neucom.2007.02.016

M3 - Article

SN - 0925-2312

VL - 71

SP - 963

EP - 972

JO - Neurocomputing

JF - Neurocomputing

IS - 4-6

ER -