In this paper we propose a model of symbolic procedure formation in the brain and its control architecture. In the model, a procedure in the brain is composed of multiple functional parts that realize subfunctions of the required computation. An internal attention vector sequence selects and activates these functional areas, and the processing circuit is realized. To evaluate the plausibility o...

The invariance of an objects' identity as it transformed over time provides a powerful cue for perceptual learning. We present an unsupervised learning procedure which maximizes the mutual information between the representations adopted by a feed-forward network at consecutive time steps. We demonstrate that the network can learn, entirely unsupervised, to classify an ensemble of several patter...

Superposition of sigmoid function over a finite time interval is shown to be equivalent to the linear combination of the solutions of a linearly parameterized system of logistic differential equations. Due to the linearity with respect to the parameters of the system, it is possible to design an effective procedure for parameter adjustment. Stability properties of this procedure are analyzed. S...

Learning a second language as an adult is particularly effortful when new phonetic representations must be formed. Therefore the processes that allow learning of speech sounds are of great theoretical and practical interest. Here we examined whether perception of single formant transitions, that is, sound components critical in speech perception, can be enhanced through an implicit task-irrelev...

The main objective of Transfer Learning is to reuse knowledge acquired in a previous learned task, in order to enhance the learning procedure in a new and more complex task. Transfer learning comprises a suitable solution for speeding up the learning procedure in Reinforcement Learning tasks. This work proposes a novel method for transferring models to Reinforcement Learning agents. The models ...

Background: Considering the assumptions of cognitive load theory and using its reduction and optimization strategies in the design and development of instructional content is one of the main requirements for achieving effective learning. This study examined the effectiveness of instructional multimedia based on reducing the extraneous cognitive load in English language learning among Nursing st...

The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors. In computer vision, success stories of learned features have been predominantly reported for object recognition tasks. In this paper, we investigate if and how feature learning can be used for material recognition. We propose two strategies to in...

In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability tables (CPTs), that quantify these networks. This increases the space of possible models, enabling the representation of CPTs with a variable num...