Criteria for optimizing cortical hierarchies with continuous ranges
In a recent paper (Reid et al.; 2009, NeuroImage) we introduced a method to calculate optimal hierarchies in the visual network that utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. Th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2010-03-01
|
| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00007/full |
| _version_ | 1819053737582264320 |
|---|---|
| author | Antje Krumnack Andrew T Reid Andrew T Reid Egon Wanke Gleb Bezgin Gleb Bezgin Rolf Kötter |
| author_facet | Antje Krumnack Andrew T Reid Andrew T Reid Egon Wanke Gleb Bezgin Gleb Bezgin Rolf Kötter |
| author_sort | Antje Krumnack |
| collection | DOAJ |
| description | In a recent paper (Reid et al.; 2009, NeuroImage) we introduced a method to calculate optimal hierarchies in the visual network that utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. There, to obtain a hierarchy, the sum of deviations from the constraints that define the hierarchy was minimized using linear optimization. In the short time since publication of that paper we noticed that many colleagues misinterpreted the meaning of the term optimal hierarchy. In particular, a majority of them were under the impression that there was perhaps only one optimal hierarchy, but a substantial difficulty in finding that one. However, there is not only more than one optimal hierarchy but also more than one option for defining optimality. Continuing the line of this work we look at additional options for optimizing the visual hierarchy: minimizing the number of violated constraints and minimizing the maximal size of a constraint violation using linear optimization and mixed integer programming. The implementation of both optimization criteria is explained in detail. In addition, using constraint sets based on the data from Felleman and Van Essen, optimal hierarchies for the visual network are calculated for both optimization methods. |
| first_indexed | 2024-12-21T12:40:29Z |
| format | Article |
| id | doaj.art-382cfc65230242a098d559d47fbf3459 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-21T12:40:29Z |
| publishDate | 2010-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-382cfc65230242a098d559d47fbf34592022-12-21T19:03:47ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962010-03-01410.3389/fninf.2010.00007971Criteria for optimizing cortical hierarchies with continuous rangesAntje Krumnack0Andrew T Reid1Andrew T Reid2Egon Wanke3Gleb Bezgin4Gleb Bezgin5Rolf Kötter6University of GiessenHeinrich Heine UniversityRadboud University Nijmegen Medical CentreHeinrich Heine UniversityHeinrich Heine UniversityRadboud University Nijmegen Medical CentreRadboud University Nijmegen Medical CentreIn a recent paper (Reid et al.; 2009, NeuroImage) we introduced a method to calculate optimal hierarchies in the visual network that utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. There, to obtain a hierarchy, the sum of deviations from the constraints that define the hierarchy was minimized using linear optimization. In the short time since publication of that paper we noticed that many colleagues misinterpreted the meaning of the term optimal hierarchy. In particular, a majority of them were under the impression that there was perhaps only one optimal hierarchy, but a substantial difficulty in finding that one. However, there is not only more than one optimal hierarchy but also more than one option for defining optimality. Continuing the line of this work we look at additional options for optimizing the visual hierarchy: minimizing the number of violated constraints and minimizing the maximal size of a constraint violation using linear optimization and mixed integer programming. The implementation of both optimization criteria is explained in detail. In addition, using constraint sets based on the data from Felleman and Van Essen, optimal hierarchies for the visual network are calculated for both optimization methods.http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00007/fullconnectivityhierarchylinear programmingmacaquemixed integer programmingoptimality |
| spellingShingle | Antje Krumnack Andrew T Reid Andrew T Reid Egon Wanke Gleb Bezgin Gleb Bezgin Rolf Kötter Criteria for optimizing cortical hierarchies with continuous ranges Frontiers in Neuroinformatics connectivity hierarchy linear programming macaque mixed integer programming optimality |
| title | Criteria for optimizing cortical hierarchies with continuous ranges |
| title_full | Criteria for optimizing cortical hierarchies with continuous ranges |
| title_fullStr | Criteria for optimizing cortical hierarchies with continuous ranges |
| title_full_unstemmed | Criteria for optimizing cortical hierarchies with continuous ranges |
| title_short | Criteria for optimizing cortical hierarchies with continuous ranges |
| title_sort | criteria for optimizing cortical hierarchies with continuous ranges |
| topic | connectivity hierarchy linear programming macaque mixed integer programming optimality |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00007/full |
| work_keys_str_mv | AT antjekrumnack criteriaforoptimizingcorticalhierarchieswithcontinuousranges AT andrewtreid criteriaforoptimizingcorticalhierarchieswithcontinuousranges AT andrewtreid criteriaforoptimizingcorticalhierarchieswithcontinuousranges AT egonwanke criteriaforoptimizingcorticalhierarchieswithcontinuousranges AT glebbezgin criteriaforoptimizingcorticalhierarchieswithcontinuousranges AT glebbezgin criteriaforoptimizingcorticalhierarchieswithcontinuousranges AT rolfkotter criteriaforoptimizingcorticalhierarchieswithcontinuousranges |