guest ::
| Home > Publications database > Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles > print |
| Home > Publications database > Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles > print |
| 001 | 916176 | ||
| 005 | 20240313103119.0 | ||
| 024 | 7 | _ | |a 10.1016/j.biosystems.2022.104813 |2 doi |
| 024 | 7 | _ | |a 0011-4014 |2 ISSN |
| 024 | 7 | _ | |a 0303-2647 |2 ISSN |
| 024 | 7 | _ | |a 1872-8324 |2 ISSN |
| 024 | 7 | _ | |a 2128/33376 |2 Handle |
| 024 | 7 | _ | |a 36460172 |2 pmid |
| 024 | 7 | _ | |a WOS:000899830200001 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-05996 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Gutzen, Robin |0 P:(DE-Juel1)171572 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles |
| 260 | _ | _ | |c 2023 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1672837690_11195 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The ”eigenangle test” quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov–Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis. |
| 536 | _ | _ | |a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) |0 G:(DE-HGF)POF4-5235 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 2 |
| 536 | _ | _ | |a HAF - Helmholtz Analytics Framework (ZT-I-0003) |0 G:(DE-HGF)ZT-I-0003 |c ZT-I-0003 |x 3 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 4 |
| 536 | _ | _ | |a Open-Access-Publikationskosten Forschungszentrum Jülich (OAPKFZJ) (491111487) |0 G:(GEPRIS)491111487 |c 491111487 |x 5 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 1 |u fzj |
| 700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 2 |
| 770 | _ | _ | |a Selected Papers from the 14th International Neural Coding Workshop, Seattle, Washington |z 0303-2647 |
| 773 | _ | _ | |a 10.1016/j.biosystems.2022.104813 |g Vol. 223, p. 104813 - |0 PERI:(DE-600)1496359-0 |p 104813 |t Biosystems |v 223 |y 2023 |x 0011-4014 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/916176/files/1-s2.0-S0303264722001940-main.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:916176 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171572 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)144168 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)144807 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5235 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | p | c | |a APC keys set |2 APC |0 PC:(DE-HGF)0000 |
| 915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2022-11-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2022-11-22 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2022-11-22 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2023-08-26 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1040 |2 StatID |b Zoological Record |d 2023-08-26 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b BIOSYSTEMS : 2022 |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-08-26 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-08-26 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2023-08-26 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a APC |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|