Diminution of Voltage Threshold Plays a Key Role in Determining Recruitment of Oculomotor Nucleus Motoneurons during Postnatal Development

The size principle dictates the orderly recruitment of motoneurons (Mns). This principle assumes that Mns of different sizes have a similar voltage threshold, cell size being the crucial property in determining neuronal recruitment. Thus, smaller neurons have higher membrane resistance and require a lower depolarizing current to reach spike threshold. However, the cell size contribution to recruitment in Mns during postnatal development remains unknown. To investigate this subject, rat oculomotor nucleus Mns were intracellularly labeled and their electrophysiological properties recorded in a brain slice preparation. Mns were divided into 2 age groups: neonatal (1–7 postnatal days, n = 14) and adult (20–30 postnatal days, n = 10). The increase in size of Mns led to a decrease in input resistance with a strong linear relationship in both age groups. A well-fitted inverse correlation was also found between input resistance and rheobase in both age groups. However, input resistance versus rheobase did not correlate when data from neonatal and adult Mns were combined in a single group. This lack of correlation is due to the fact that decrease in input resistance of developing Mns did not lead to an increase in rheobase. Indeed, a diminution in rheobase was found, and it was accompanied by an unexpected decrease in voltage threshold. Additionally, the decrease in rheobase co-varied with decrease in voltage threshold in developing Mns. These data support that the size principle governs the recruitment order in neonatal Mns and is maintained in adult Mns of the oculomotor nucleus; but during postnatal development the crucial property in determining recruitment order in these Mns was not the modifications of cell size-input resistance but of voltage threshold

Nonsmooth optimization based algorithms in cluster analysis

Cluster analysis is an important task in data mining. It deals with the problem of organization of a collection of objects into clusters based on a similarity measure. Various distance functions can be used to define the similarity measure. Cluster analysis problems with the similarity measure defined by the squared Euclidean distance, which is also known as the minimum sum-of-squares clustering, has been studied extensively over the last five decades. L1 and L1 norms have attracted less attention. In this chapter, we consider a nonsmooth nonconvex optimization formulation of the cluster analysis problems. This formulation allows one to easily apply similarity measures defined using different distance functions. Moreover, an efficient incremental algorithm can be designed based on this formulation to solve the clustering problems. We develop incremental algorithms for solving clustering problems where the similarity measure is defined using the L1; L2 and L1 norms. We also consider different algorithms for solving nonsmooth nonconvex optimization problems in cluster analysis. The proposed algorithms are tested using several real world data sets and compared with other similar algorithms

Location Software and Interface with GIS and Supply Chain Management

The objective of this paper is to bridge the gap between location theory and practice. To meet this objective focus is given to the development of software capable of addressing the different needs of a wide group of users. There is a very active community on location theory encompassing many research fields such as operations research, computer science, mathematics, engineering, geography, economics and marketing. As a result, people working on facility location problems have a very diverse background and also different needs regarding the software to solve these problems. For those interested in non-commercial applications (e. g. students and researchers), the library of location algorithms (LoLA can be of considerable assistance. LoLA contains a collection of efficient algorithms for solving planar, network and discrete facility location problems. In this paper, a detailed description of the functionality of LoLA is presented. In the fields of geography and marketing, for instance, solving facility location problems requires using large amounts of demographic data. Hence, members of these groups (e. g. urban planners and sales managers) often work with geographical information too s. To address the specific needs of these users, LoLA was inked to a geographical information system (GIS) and the details of the combined functionality are described in the paper. Finally, there is a wide group of practitioners who need to solve large problems and require special purpose software with a good data interface. Many of such users can be found, for example, in the area of supply chain management (SCM). Logistics activities involved in strategic SCM include, among others, facility location planning. In this paper, the development of a commercial location software tool is also described. The too is embedded in the Advanced Planner and Optimizer SCM software developed by SAP AG, Walldorf, Germany. The paper ends with some conclusions and an outlook to future activities