We present a framework for the self-organized formation of high level learning by a statistical pre-processing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, opti...

Unsupervised Hebbian learning produces a scale invariant associative phenomena with limited computational power (the linear separation of states). With the inclusion of a Liquid State Machine (LSM), simple associative methods are transformed to provide truly general purpose and computationally powerful ontogenetic learning. This radical expansion of the representational space of a problem enabl...

We measured local field potential (LFP) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in the medial temporal lobes of monkeys and humans, respectively, as they performed the same conditional motor associative learning task. Parallel analyses were used to examine both data sets. Despite significantly faster learning in humans relative to monkeys, we found e...

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, r...

The neural correlates of associative memory in healthy older adults were investigated by examining the correlation of associative memory performance with spontaneous brain oscillations. Eighty healthy older adults underwent a resting-state functional MRI and took a paired-associative learning test (PALT). Correlations between the amplitude of low-frequency fluctuations (ALFF) as well as fractio...