Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “sca...

Descripción completa

Detalles Bibliográficos
Autores principales: Prettejohn, Brenton J., Berryman, Matthew J., McDonnell, Mark D.
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059456/
https://www.ncbi.nlm.nih.gov/pubmed/21441986
http://dx.doi.org/10.3389/fncom.2011.00011
_version_ 1782200409864011776
author Prettejohn, Brenton J.
Berryman, Matthew J.
McDonnell, Mark D.
author_facet Prettejohn, Brenton J.
Berryman, Matthew J.
McDonnell, Mark D.
author_sort Prettejohn, Brenton J.
collection PubMed
description Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.
format Text
id pubmed-3059456
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Frontiers Research Foundation
record_format MEDLINE/PubMed
spelling pubmed-30594562011-03-25 Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists Prettejohn, Brenton J. Berryman, Matthew J. McDonnell, Mark D. Front Comput Neurosci Neuroscience Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks. Frontiers Research Foundation 2011-03-10 /pmc/articles/PMC3059456/ /pubmed/21441986 http://dx.doi.org/10.3389/fncom.2011.00011 Text en Copyright © 2011 Prettejohn, Berryman and McDonnell. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and Frontiers Media SA, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
spellingShingle Neuroscience
Prettejohn, Brenton J.
Berryman, Matthew J.
McDonnell, Mark D.
Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists
title Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists
title_full Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists
title_fullStr Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists
title_full_unstemmed Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists
title_short Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists
title_sort methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059456/
https://www.ncbi.nlm.nih.gov/pubmed/21441986
http://dx.doi.org/10.3389/fncom.2011.00011
work_keys_str_mv AT prettejohnbrentonj methodsforgeneratingcomplexnetworkswithselectedstructuralpropertiesforsimulationsareviewandtutorialforneuroscientists
AT berrymanmatthewj methodsforgeneratingcomplexnetworkswithselectedstructuralpropertiesforsimulationsareviewandtutorialforneuroscientists
AT mcdonnellmarkd methodsforgeneratingcomplexnetworkswithselectedstructuralpropertiesforsimulationsareviewandtutorialforneuroscientists