Bottom-up learning of explicit knowledge using a Bayesian algorithm and a new Hebbian learning rule.

The goal of this article is to propose a new cognitive model that focuses on bottom-up learning of explicit knowledge (i.e., the transformation of implicit knowledge into explicit knowledge).

NDRAM: nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns.

This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule.

Behavioral avoidance dynamics in the presence of a virtual spider.

Tracking behavior with a virtual spider and a neutral target is compared in fearful and nonfearful subjects. Head-tracking in virtual environments appears to be a scale-free behavior with long-range fractal-like patterns. Moreover, these fractal patterns change according to what the target affords the tracker and the level of behavioral avoidance manifested by the subjects.