Biomedical subjects

Sylvain Chartier

Publications and source records attributed to Sylvain Chartier.

4 recordsLinked to original sources

PubMed2006

A sequential dynamic heteroassociative memory for multistep pattern recognition and one-to-many association.

Bidirectional associative memories (BAMs) have been widely used for auto and heteroassociative learning. However, few research efforts have addressed the issue of multistep vector pattern recognition. We propose a model that can perform multi step pattern recognition without the need for a special learning algorithm, and with the capacity to learn more than two pattern series in the training set. The model can also learn pattern series of different lengths and, contrarily to previous models, the stimuli can be composed of gray-level images. The paper also shows that by adding an extra autoassociative layer, the model can accomplish one-to-many association, a task that was exclusive to feedforward networks with context units and error backpropagation learning.

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PubMed2006

A bidirectional heteroassociative memory for binary and grey-level patterns.

Typical bidirectional associative memories (BAM) use an offline, one-shot learning rule, have poor memory storage capacity, are sensitive to noise, and are subject to spurious steady states during recall. Recent work on BAM has improved network performance in relation to noisy recall and the number of spurious attractors, but at the cost of an increase in BAM complexity. In all cases, the networks can only recall bipolar stimuli and, thus, are of limited use for grey-level pattern recall. In this paper, we introduce a new bidirectional heteroassociative memory model that uses a simple self-convergent iterative learning rule and a new nonlinear output function. As a result, the model can learn online without being subject to overlearning. Our simulation results show that this new model causes fewer spurious attractors when compared to others popular BAM networks, for a comparable performance in terms of tolerance to noise and storage capacity. In addition, the novel output function enables it to learn and recall grey-level patterns in a bidirectional way.

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PubMed2005

Scene perception, gaze behavior, and perceptual learning in virtual environments.

More and more immersive environments are developed to provide support for learning or training purposes. Ecological validity of such environments is usually based on learning performance comparisons between virtual environments and their genuine counterparts. Little is known about learning processes occurring in immersive environments. A new technique is proposed for testing perceptual learning during virtual immersion. This methodology relies upon eye-tracking technologies to analyze gaze behavior recorded in relation to virtual objects' features and tasks' requirements. It is proposed that perceptual learning mechanisms engaged could be detected through eye movements. In this study, nine subjects performed perceptual learning tasks in virtual immersion. Results obtained indicated that perceptual learning influences gaze behavior dynamics. More precisely, analysis revealed that fixation number and variability in fixation duration varied with perceptual learning level. Such findings could contribute in shedding light on learning mechanisms as well as providing additional support for validating virtual learning environments.

Adult↗

PubMed2005

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. Several computer simulations show the model's distinguishing properties.

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