7,124 research outputs found
Relation Structure moléculaire - Odeur Utilisation des Réseaux de Neurones pour l’estimation de l’Odeur Balsamique
Les molécules odorantes (parfums ou flaveurs) sont utilisées dans une grande variété de produits de consommation, pour inciter les consommateurs à associer les impressions favorables à un produit donné. La Relation Structure moléculaire-Odeur (SOR) est cruciale pour la synthèse de ces molécules mais est très difficile à établir due à la subjectivité de l’odeur. Ce travail présente une approche de prédiction de l'odeur des molécules basée sur les descripteurs moléculaires. Les techniques d’analyse en composantes principales (PCA) et de d’analyse de colinéarité permettent d’identifier les descripteurs les plus pertinents. un réseau de neurones supervisé5 à deux couches (cachée et sortie) est employé pour corréler la structure moléculaire à l’odeur. La base de données décrite précédemment est utilisée pour l’apprentissage. Un ensemble de paramètres est modifié jusqu’à la satisfaction de la meilleure régression.
Les résultats obtenus sont encouragent, ainsi les descripteurs moléculaires convenables corrèlent efficacement l'odeur des molécules. C’est la première étape d’un modèle générique en développement pour corréler l'odeur avec les structures moléculaire
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding
different relationships and their interactions in a complex system, where each
layer in a multilayer network depicts the topological structure of a group of
nodes corresponding to a particular relationship. The interactions among
different layers imply how the interplay of different relations on the topology
of each layer. For a single-layer network, network embedding methods have been
proposed to project the nodes in a network into a continuous vector space with
a relatively small number of dimensions, where the space embeds the social
representations among nodes. These algorithms have been proved to have a better
performance on a variety of regular graph analysis tasks, such as link
prediction, or multi-label classification. In this paper, by extending a
standard graph mining into multilayer network, we have proposed three methods
("network aggregation," "results aggregation" and "layer co-analysis") to
project a multilayer network into a continuous vector space. From the
evaluation, we have proved that comparing with regular link prediction methods,
"layer co-analysis" achieved the best performance on most of the datasets,
while "network aggregation" and "results aggregation" also have better
performance than regular link prediction methods
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