From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity
dc.contributor.author | Fletcher, JM | |
dc.contributor.author | Wennekers, Thomas | |
dc.date.accessioned | 2017-02-13T15:05:47Z | |
dc.date.accessioned | 2017-08-10T10:21:08Z | |
dc.date.available | 2017-02-13T15:05:47Z | |
dc.date.available | 2017-08-10T10:21:08Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 0129-0657 | |
dc.identifier.issn | 1793-6462 | |
dc.identifier.other | 1750013 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/9713 | |
dc.description.abstract |
It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory–inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures. | |
dc.format.extent | 0-0 | |
dc.format.medium | Print-Electronic | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | World Scientific Pub Co Pte Lt | |
dc.relation.replaces | http://hdl.handle.net/10026.1/8463 | |
dc.relation.replaces | 10026.1/8463 | |
dc.subject | Spiking neurons | |
dc.subject | network topology | |
dc.subject | network centrality | |
dc.subject | Katz centrality | |
dc.subject | PageRank | |
dc.subject | structure function relationship | |
dc.title | From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000423207800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 02 | |
plymouth.volume | 28 | |
plymouth.publication-status | Published | |
plymouth.journal | International Journal of Neural Systems | |
dc.identifier.doi | 10.1142/S0129065717500137 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Admin Group - REF | |
plymouth.organisational-group | /Plymouth/Admin Group - REF/REF Admin Group - FoSE | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.publisher.place | Singapore | |
dcterms.dateAccepted | 2016-11-07 | |
dc.identifier.eissn | 1793-6462 | |
dc.rights.embargoperiod | No embargo | |
rioxxterms.funder | EPSRC | |
rioxxterms.identifier.project | BABEL | |
rioxxterms.versionofrecord | 10.1142/S0129065717500137 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2018 | |
rioxxterms.type | Journal Article/Review | |
plymouth.funder | BABEL::EPSRC | |
plymouth.oa-location | http://www.worldscientific.com/doi/abs/10.1142/S0129065717500137 |