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 Erd"{o}s-R'{e}nyi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for examp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-03-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00011/full |
| _version_ | 1819273904755048448 |
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| author | Brenton J Prettejohn Matthew J Berryman Mark D McDonnell |
| author_facet | Brenton J Prettejohn Matthew J Berryman Mark D McDonnell |
| author_sort | Brenton J Prettejohn |
| collection | DOAJ |
| description | Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erd"{o}s-R'{e}nyi 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. |
| first_indexed | 2024-12-23T22:59:57Z |
| format | Article |
| id | doaj.art-24dc287e7ef345beac6e409efdd875aa |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-23T22:59:57Z |
| publishDate | 2011-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-24dc287e7ef345beac6e409efdd875aa2022-12-21T17:26:55ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882011-03-01510.3389/fncom.2011.000118783Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientistsBrenton J Prettejohn0Matthew J Berryman1Mark D McDonnell2University of South AustraliaUniversity of South AustraliaUniversity of South AustraliaMany simulations of networks in computational neuroscience assume completely homogenous random networks of the Erd"{o}s-R'{e}nyi 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.http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00011/fullcomplex networksbrain networkscortical networksdirected networknetwork simulationscale-free network |
| spellingShingle | Brenton J Prettejohn Matthew J Berryman Mark D McDonnell Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists Frontiers in Computational Neuroscience complex networks brain networks cortical networks directed network network simulation scale-free network |
| 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 | complex networks brain networks cortical networks directed network network simulation scale-free network |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00011/full |
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