Biomedical subjects

Bernard Doyon

Publications and source records attributed to Bernard Doyon.

4 recordsLinked to original sources

PubMed2004

Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three-dimensional resistor mesh model.

Bone thickness, anisotropy, and inhomogeneity have been reported to induce important variations in electroencephalogram (EEG) scalp potentials. To study this effect, we used an original three-dimensional (3-D) resistor mesh model described in spherical coordinates, consisting of 67,464 elements and 22,105 nodes arranged in 36 different concentric layers. After validation of the model by comparison with the analytic solution, potential variations induced by geometric and electrical skull modifications were investigated at the surface in the dipole plane and along the dipole axis, for several eccentricities and bone thicknesses. The resistor mesh permits one to obtain various configurations, as local modifications are introduced very easily. This has allowed several head models to be designed to study the effects of skull properties (thickness, anisotropy, and heterogeneity) on scalp surface potentials. Results show a decrease of potentials in bone, depending on bone thickness, and a very small decrease through the scalp layer. Nevertheless, similar scalp potentials can be obtained using either a thick scalp layer and a thin skull layer, and vice versa. It is thus important to take into account skull and scalp thicknesses, because the drop of potential in bone depends on both. The use of three different layers for skull instead of one leads to small differences in potential values and patterns. In contrast, the introduction of a hole in the skull highly increases the maximum potential value (by a factor of 11.5 in our case), because of the absence of potential drop in the corresponding volume. The inverse solution without any a priori knowledge indicates that the model with the hole gives the largest errors in both position and dipolar moment. Our results indicate that the resistor mesh model can be used as a robust and user-friendly simulation tool in EEG or event-related potentials. It makes it possible to build up real head models directly from anatomic magnetic resonance imaging without tessellation, and is able to take into account head heterogeneities very simply by changing volume elements conductivity. Hum. Brain Mapping 21:84-95, 2004.

Anisotropy↗

PubMed2003

Neural timing of visual implicit categorization.

Most of the neuroimaging studies that have shown visual category-specific activations or categorization effects have been based on a subtractive approach. In the present study, we investigated, by means of EEG, not only the net result of the categorization but also the dynamics of the process. Subjects had to perform a target detection task throughout an image set of distractors belonging to six categories: letters, geometrical figures, faces, tools, structured textures and Asiatic characters. Multivariate analyses were performed on the responses to the non-target stimuli according to their category. Categorical neural responses were only obtained on P2 latencies and N2 amplitudes. This result suggests that there are no differences in the first stage of the implicit categorization of the distractors (visual analysis and proximal stimulus representation elaboration from 100 to 220 ms) and that differences appear between 220 and 280 ms (matching to structural representations). Over-learned stimuli (e.g. letters) elicited the shortest P2 latency, contrasting with unknown categories (e.g. Asiatic characters) that revealed the longest P2 latencies and flattened N2 waves. Categorical differences indicate that the more a subject knows about an object, the less cognitive resources are used. In conclusion, our results suggest that a reduction in neural activity could reflect an improved accuracy in cognitive and cortical processing.

Adult↗

PubMed2002

Resonant spatiotemporal learning in large random recurrent networks.

Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present some hypotheses on the computational role of feedback connections.

Brain↗

PubMed1998

Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning.

Freeman's investigations on the olfactory bulb of the rabbit showed that its signal dynamics was chaotic, and that recognition of a learned stimulus is linked to a dimension reduction of the dynamics attractor. In this paper we address the question whether this behavior is specific of this particular architecture, or if it is a general property. We study the dynamics of a non-convergent recurrent model-the random recurrent neural networks. In that model a mean-field theory can be used to analyze the autonomous dynamics. We extend this approach with various observations on significant changes in the dynamical regime when sending static random stimuli. Then we propose a Hebb-like learning rule, viewed as a self-organization dynamical process inducing specific reactivity to one random stimulus. We numerically show the dynamics reduction during learning and recognition processes and analyze it in terms of dynamical repartition of local neural activity.

Journal Article↗