The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated. Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed. Two autonomous robots wer...
Humans instantly recognize a previously seen face as "familiar." To deepen our understanding of familiarity-novelty detection, we simulated biologically plausible neural network models of generic cortical microcircuits consisting of spiking neurons w...
The international journal of medical robotics + computer assisted surgery : MRCAS
May 16, 2017
BACKGROUND: Few studies compare the effectiveness of blocked vs random practice conditions in minimally invasive surgery training, and none have evaluated these in robotic surgery training.
Neural networks : the official journal of the International Neural Network Society
May 9, 2017
Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends...
Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale
May 8, 2017
We explore the ability of a very simple artificial neural network, a perceptron, to assert the musical key of novel stimuli. First, perceptrons are trained to associate standardized key profiles (taken from 1 of 3 different sources) to different musi...
Neural networks : the official journal of the International Neural Network Society
Apr 27, 2017
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified...
Neural networks : the official journal of the International Neural Network Society
Apr 25, 2017
Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Convent...
Humans regularly perform new learning without losing memory for previous information, but neural network models suffer from the phenomenon of catastrophic forgetting in which new learning impairs prior function. A recent article presents an algorithm...
One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can nat...