Attractor learning in synchronized chaotic systems in the presence of unresolved scales.

Recently, supermodels consisting of an ensemble of interacting models, synchronizing on a common solution, have been proposed as an alternative to the common non-interactive multi-model ensembles in order to improve climate predictions. The connection terms in the interacting ensemble are to be optimized based on the data. The supermodel approach has been successfully demonstrated in a number of simulation experiments with an assumed ground truth and a set of good, but imperfect models.

The potential of a portable, point-of-care electronic nose to diagnose tuberculosis.

Introduction: Tuberculosis (TB) is the leading cause of death due to an infectious disease worldwide. Especially in low-income countries, new diagnostic techniques that are accessible, inexpensive and easy-to-use, are needed to shorten transmission time and initiate treatment earlier.

Objective: We conducted a study with a handheld, point-of-care electronic nose (eNose) device to diagnose TB through exhaled breath.

Time-integrated position error accounts for sensorimotor behavior in time-constrained tasks.

Several studies have shown that human motor behavior can be successfully described using optimal control theory, which describes behavior by optimizing the trade-off between the subject's effort and performance. This approach predicts that subjects reach the goal exactly at the final time. However, another strategy might be that subjects try to reach the target position well before the final time to avoid the risk of missing the target.

A development protocol for a diagnostic DSS.

A diagnostic decision support system (DSS) in medicine is an expert system that aids the physician in the determination of the diagnosis based on findings and test results. The DSS can be divided into 2 different types of components: the knowledge component and the information system component. Methods from software engineering, knowledge engineering and management are combined into a dynamic development cycle that allows stepwise update and refinement.

A neural network study of precollicular saccadic averaging.

Saccadic averaging is the phenomenon that two simultaneously presented retinal inputs result in a saccade with an endpoint located on an intermediate position between the two stimuli. Recordings from neurons in the deeper layers of the superior colliculus have revealed neural correlates of saccade averaging, indicating that it takes place at this level or upstream. Recently, we proposed a neural network for internal feedback in saccades.

How dependencies between successive examples affect on-line learning.

We study the dynamics of on-line learning for a large class of neural networks and learning rules, including backpropagation for multilayer perceptrons. In this paper, we focus on the case where successive examples are dependent, and we analyze how these dependencies affect the learning process. We define the representation error and the prediction error.