Criteria for optimizing cortical hierarchies with continuous ranges

Abstract

In a recent paper (Reid et al., 2009) 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 fi t fi xed hierarchical distances. There, to obtain a hierarchy, the sum of deviations from the constraints that defi ne 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 diffi culty in fi nding that one. However, there is not only more than one optimal hierarchy but also more than one option for defi ning 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 (1991), optimal hierarchies for the visual network are calculated for both optimization methods. © 2010 Krumnack, Reid, Wanke, Bezgin and Kötter.