Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures
The neuroanatomical connectivity of cortical circuits is believed to follow certain rules, the exact origins of which are still poorly understood. In particular, numerous nonrandom features, such as common neighbor clustering, overrepresentation of reciprocal connectivity, and overrepresentation of...
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2014-11-01
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Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnana.2014.00125/full |
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doaj-9c349f48a87f415da4666a223a0e1b0d2020-11-24T23:23:10ZengFrontiers Media S.A.Frontiers in Neuroanatomy1662-51292014-11-01810.3389/fnana.2014.0012593608Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structuresDaniel Carl Miner0Jochen eTriesch1Frankfurt Institute for Advanced StudiesFrankfurt Institute for Advanced StudiesThe neuroanatomical connectivity of cortical circuits is believed to follow certain rules, the exact origins of which are still poorly understood. In particular, numerous nonrandom features, such as common neighbor clustering, overrepresentation of reciprocal connectivity, and overrepresentation of certain triadic graph motifs have been experimentally observed in cortical slice data. Some of these data, particularly regarding bidirectional connectivity are seemingly contradictory, and the reasons for this are unclear. Here we present a simple static geometric network model with distance-dependent connectivity on a realistic scale that naturally gives rise to certain elements of these observed behaviors, and may provide plausible explanations for some of the conflicting findings. Specifically, investigation of the model shows that experimentally measured nonrandom effects, especially bidirectional connectivity, may depend sensitively on experimental parameters such as slice thickness and sampling area, suggesting potential explanations for the seemingly conflicting experimental results.http://journal.frontiersin.org/Journal/10.3389/fnana.2014.00125/fullSamplingcortical networksgraph theorymotifsnetwork topologyslice |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel Carl Miner Jochen eTriesch |
spellingShingle |
Daniel Carl Miner Jochen eTriesch Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures Frontiers in Neuroanatomy Sampling cortical networks graph theory motifs network topology slice |
author_facet |
Daniel Carl Miner Jochen eTriesch |
author_sort |
Daniel Carl Miner |
title |
Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures |
title_short |
Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures |
title_full |
Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures |
title_fullStr |
Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures |
title_full_unstemmed |
Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures |
title_sort |
slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroanatomy |
issn |
1662-5129 |
publishDate |
2014-11-01 |
description |
The neuroanatomical connectivity of cortical circuits is believed to follow certain rules, the exact origins of which are still poorly understood. In particular, numerous nonrandom features, such as common neighbor clustering, overrepresentation of reciprocal connectivity, and overrepresentation of certain triadic graph motifs have been experimentally observed in cortical slice data. Some of these data, particularly regarding bidirectional connectivity are seemingly contradictory, and the reasons for this are unclear. Here we present a simple static geometric network model with distance-dependent connectivity on a realistic scale that naturally gives rise to certain elements of these observed behaviors, and may provide plausible explanations for some of the conflicting findings. Specifically, investigation of the model shows that experimentally measured nonrandom effects, especially bidirectional connectivity, may depend sensitively on experimental parameters such as slice thickness and sampling area, suggesting potential explanations for the seemingly conflicting experimental results. |
topic |
Sampling cortical networks graph theory motifs network topology slice |
url |
http://journal.frontiersin.org/Journal/10.3389/fnana.2014.00125/full |
work_keys_str_mv |
AT danielcarlminer slicingsamplinganddistancedependenteffectsaffectnetworkmeasuresinsimulatedcorticalcircuitstructures AT jochenetriesch slicingsamplinganddistancedependenteffectsaffectnetworkmeasuresinsimulatedcorticalcircuitstructures |
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1725564921595297792 |