Comput. Biol. Med. - Odorant recognition using biological responses recorded in olfactory bulb of rats.

Tópicos

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Resumo

In this study we applied pattern recognition (PR) techniques to extract odorant information from local field potential (LFP) signals recorded in the olfactory bulb (OB) of rats subjected to different odorant stimuli. We claim that LFP signals registered on the OB, the first stage of olfactory processing, are stimulus specific in animals with normal sensory experience, and that these patterns correspond to the neural substrate likely required for perceptual discrimination. Thus, these signals can be used as input to an artificial odorant classification system with great success. In this paper we have designed and compared the performance of several configurations of artificial olfaction systems (AOS) based on the combination of four feature extraction (FE) methods (Principal Component Analysis (PCA), Fisher Transformation (FT), Sammon NonLinear Map (NLM) and Wavelet Transform (WT)), and three PR techniques (Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP) and Support Vector Machine (SVM)), when four different stimuli are presented to rats. The best results were reached when PCA extraction followed by SVM as classifier were used, obtaining a classification accuracy of over 95% for all four stimuli.

Resumo Limpo

studi appli pattern recognit pr techniqu extract odor inform local field potenti lfp signal record olfactori bulb ob rat subject differ odor stimuli claim lfp signal regist ob first stage olfactori process stimulus specif anim normal sensori experi pattern correspond neural substrat like requir perceptu discrimin thus signal can use input artifici odor classif system great success paper design compar perform sever configur artifici olfact system ao base combin four featur extract fe method princip compon analysi pca fisher transform ft sammon nonlinear map nlm wavelet transform wt three pr techniqu linear discrimin analysi lda multilay perceptron mlp support vector machin svm four differ stimuli present rat best result reach pca extract follow svm classifi use obtain classif accuraci four stimuli

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