Convolutional neural network for classification of nerve activity based on action potential induced neurochemical Signatures

Neural activity results in chemical changes in the extracellular environment such as variation in pH or potassium/ sodium ion concentration. Higher signal to noise ratio make neurochemical signals an interesting biomarker for closed-loop neuromodulation systems. For such applications, it is important to reliably classify pH signatures to control stimulation timing and possibly dosage. For example, the activity of the subdiaphragmatic vagus nerve (sVN) branch can be monitored by measuring extracellular neural pH. More importantly, gut hormone cholecystokinin (CCK)-specific activity on the sVN can be used for controllably activating sVN, in order to mimic the gut-brain neural response to food intake. In this paper, we present a convolutional neural network (CNN) based classification system to identify CCK-specific neurochemical changes on the sVN, from non-linear background activity. Here we present a novel feature engineering approach which enables, after training, a high accuracy classification of neurochemical signals using CNN

Swifterbant S4 (the Netherlands):Occupation and exploitation of a Neolithic levee site (c. 4300-4000 cal. BC)

This publication presents the results of the 2005-2007 excavations at Swifterbant S4, carried out by the Groningen INstitute of Archaeology. S4 is a well-preserved Neolthic wetland site (c. 4300-4000 cal. BC) located within the Swifterbant river system in the Netherlands. We present the landscape setting, the various find categories and the spatial patterns with three research themes in mind. Theme 1 concerns the environmental setting, subsistence and site function. We conclude that the Swifterbant hunter-gatherer-farmers exploited a mosaic-type landscape. Theme 2 deals with developments in site function during the exploitation and exploitation history of the site. This analysis leads to the observation that episodes of cultivation and settlement alternated at S4. Theme 3, the use of space, was difficult to study due to the fragmented nature of the excavation plan. This site monograph makes Swifterbant S4 the most comprehensively published site of the Swifterbant river system

Composition mechanisms for retrenchment

Retrenchment is a flexible model evolution formalism that arose as a reaction to the limitations imposed by refinement, and for which the proof obligations feature additional predicates for accommodating design data. Composition mechanisms for retrenchment are studied. Vertical, horizontal, dataflow, parallel and fusion compositions are described. Of particular note are the means by which the additional predicates compose. It is argued that all of the compositions introduced are associative, and that they are mutually coherent. Composition of retrenchment with refinement, so important for the smooth interworking of the two techniques, is discussed. Decomposition, allowing finer grained retrenchments to be extracted from a single large grained retrenchment, is also investigated