Most connectionist or "neural network" learning systems use some form of the back-propagation algorithm. However, back-propagation learning is too slow for many applications, and it scales up poorly as tasks become larger and more complex. The factors governing learning speed are poorly

that can conceptualize the different effects of rewards on behavior. The scientific investigation of behavioral processes by animal learning theory and economic utility theory has produced a theoretical framework that can help to elucidate the neural correlates for reward functions in learning, goal

The objectives of this project were to: i) describe the experience of implementing Problem-Based Learning in an online course over three consecutive academic years, ii) analyse the learning environment generated, iii) discuss impacts on students ’ active participation, based on the analysis

resulting from their volatile and voluntary participation structure. This article aims to examine discussions that took place in MobiMOOC in the spring of 2011, a MOOC structured around mobile learning. This line of inquiry focused on language from the discussions that contained emotive vocabulary

intentions to drop out of high school, which are later implemented, leading to actual dropout behavior. This model was tested with high school students (N = 4,537) by means of a prospective design. Results from analyses of variance and a structural equation modeling analysis (with L1SREL) were found

, direct observation, and microanalyses. Although still in the formative stage of devel-opment, these online measures are providing valuable new information regarding the causal impact of SRL processes as well as raising new questions for future study. KEYWORDS: self-regulated learning (SRL), motivation

DNA to be sequences into distinct pieces, parcel out the detailed work of sequencing, and then reassemble these independent efforts at the end. It is not quite so simple in the world of genome semantics. Despite the differences between genome sequencing and genetic network discovery, there are clear parallels that are illustrated in To enable this process, there will need to be standardized forms for model representation. At present, there are many different modeling technologies in use, and although models can be easily placed into a database, they are not useful out of the context of their specific modeling package. The need for a standardized way of communicating computational descriptions of biological systems extends to the literature. Entire conferences have been established to explore ways of mining the biology literature to extract semantic information in computational form. Going forward, as a community we need to come to consensus on how to represent what we know about biology in computational form as well as in words. The key to postgenomic biology will be the computational assembly of our collective knowledge into a cohesive picture of cellular and organism function. With such a comprehensive model, we will be able to explore new types of conservation between organisms and make great strides toward new therapeutics that function on well-characterized pathways.

images either as the sole source of training data for building an implicit 3D representation, or by enriching 3D models with natural image features. In this paper, we go back to the ideas from the early days of computer vision, by using 3D object models as the only source of information for building a

Back to the future: synaesthesia could be due to associative

This review puts into perspective the present state and prospects for controlling quantum phenomena in atoms and molecules. The topics considered include the nature of physical and chemical control objectives, the development of possible quantum control rules of thumb, the theoretical design