Optimal hierarchical modular topologies for producing limited sustained activation of neural networks
An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the...
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doaj-adfde330299a452d99cf0d63ce421e3c2020-11-24T21:40:27ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962010-05-01410.3389/fninf.2010.00008713Optimal hierarchical modular topologies for producing limited sustained activation of neural networksMarcus Kaiser0Marcus Kaiser1Marcus Kaiser2Claus C Hilgetag3Claus C Hilgetag4Newcastle UniversityNewcastle UniversitySeoul National UniversityBoston UniversityJacobs University BremenAn essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show a wider parameter range for LSA than random or small-world networks not possessing hierarchical organization or multiple modules. Here we explored how variation in the number of hierarchical levels and modules per level influenced network dynamics and occurrence of LSA. We tested hierarchical configurations of different network sizes, approximating the large-scale networks linking cortical columns in one hemisphere of the rat, cat, or macaque monkey brain. Scaling of the network size affected the number of hierarchical levels and modules in the optimal networks, also depending on whether global edge density or the numbers of connections per node were kept constant. For constant edge density, only few network configurations, possessing an intermediate number of levels and a large number of modules, led to a large range of LSA independent of brain size. For a constant number of node connections, there was a trend for optimal configurations in larger-size networks to possess a larger number of hierarchical levels or more modules. These results may help to explain the trend to greater network complexity apparent in larger brains and may indicate that this complexity is required for maintaining stable levels of neural activation.http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00008/fullCerebral Cortexmodularityneural networksbrain connectivityneural dynamicsfunctional criticality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marcus Kaiser Marcus Kaiser Marcus Kaiser Claus C Hilgetag Claus C Hilgetag |
spellingShingle |
Marcus Kaiser Marcus Kaiser Marcus Kaiser Claus C Hilgetag Claus C Hilgetag Optimal hierarchical modular topologies for producing limited sustained activation of neural networks Frontiers in Neuroinformatics Cerebral Cortex modularity neural networks brain connectivity neural dynamics functional criticality |
author_facet |
Marcus Kaiser Marcus Kaiser Marcus Kaiser Claus C Hilgetag Claus C Hilgetag |
author_sort |
Marcus Kaiser |
title |
Optimal hierarchical modular topologies for producing limited sustained activation of neural networks |
title_short |
Optimal hierarchical modular topologies for producing limited sustained activation of neural networks |
title_full |
Optimal hierarchical modular topologies for producing limited sustained activation of neural networks |
title_fullStr |
Optimal hierarchical modular topologies for producing limited sustained activation of neural networks |
title_full_unstemmed |
Optimal hierarchical modular topologies for producing limited sustained activation of neural networks |
title_sort |
optimal hierarchical modular topologies for producing limited sustained activation of neural networks |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroinformatics |
issn |
1662-5196 |
publishDate |
2010-05-01 |
description |
An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show a wider parameter range for LSA than random or small-world networks not possessing hierarchical organization or multiple modules. Here we explored how variation in the number of hierarchical levels and modules per level influenced network dynamics and occurrence of LSA. We tested hierarchical configurations of different network sizes, approximating the large-scale networks linking cortical columns in one hemisphere of the rat, cat, or macaque monkey brain. Scaling of the network size affected the number of hierarchical levels and modules in the optimal networks, also depending on whether global edge density or the numbers of connections per node were kept constant. For constant edge density, only few network configurations, possessing an intermediate number of levels and a large number of modules, led to a large range of LSA independent of brain size. For a constant number of node connections, there was a trend for optimal configurations in larger-size networks to possess a larger number of hierarchical levels or more modules. These results may help to explain the trend to greater network complexity apparent in larger brains and may indicate that this complexity is required for maintaining stable levels of neural activation. |
topic |
Cerebral Cortex modularity neural networks brain connectivity neural dynamics functional criticality |
url |
http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00008/full |
work_keys_str_mv |
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