Training selective use of a complex categorisation strategy

The present study aims to encourage selective use of a complex categorisation strategy. More specifically, participants will be trained to use a two dimensional strategy in one region of category space and a more complex three-dimensional strategy in another region of category space. In the 2–3 conditions, participants will be presented with stimuli requiring the two-dimensional strategy in the first phase of training and the three-dimensional strategy in the second phase of training. In the 3-2 conditions, participants will be presented with stimuli requiring the three-dimensional strategy in the first phase of training and the two-dimensional strategy in the second phase of training. The main dependent measure will be performance on exceptions to the two-dimensional strategy. If participants learn to selectively use the three-dimensional strategy, then we expect them to correctly classify novel exceptions that occur in the three-dimensional region of the category space and incorrectly classify novel exceptions that occur in the two-dimensional region of the category space.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.