In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of...In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.展开更多
Obtaining an electrocorticograms(ECoG)signal requires an invasive procedure in which brain activity is recorded from the cortical surface.In contrast,obtaining electroencephalograms(EEG)recordings requires the non-inv...Obtaining an electrocorticograms(ECoG)signal requires an invasive procedure in which brain activity is recorded from the cortical surface.In contrast,obtaining electroencephalograms(EEG)recordings requires the non-invasive procedure of recording the brain activity from the scalp surface,which allows EEG recordings to be performed more easily on healthy humans.In this work,a technique previously used to study spatial-temporal patterns of brain activity on animal ECoG was adapted for use on EEG.The main issues are centered on solving the problems introduced by the increment on the interelectrode distance and the procedure to detect stable frames.The results showed that spatial patterns of beta and gamma activity can also be extracted from the EEG signal by using stable frames as time markers for feature extraction.This adapted technique makes it possible to take advantage of the cognitive and phenomenological awareness of a normal healthy subject.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 60874098 and 60911130129)the High-Tech Research and Development Program (863) of China (No. 2007AA042103)+1 种基金the National Creative Research Groups Science Foundation of China (No. 60721062)the Project of Introducing Talents for Chinese University Disciplinal Innovation (111 Project, No. B07031)
文摘In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.
基金supported by the National Natural Science Foundation of China(Nos.60421002 and 60874098)the National High-Tech Research and Development Program(863)of China(No.2007AA042103)
文摘Obtaining an electrocorticograms(ECoG)signal requires an invasive procedure in which brain activity is recorded from the cortical surface.In contrast,obtaining electroencephalograms(EEG)recordings requires the non-invasive procedure of recording the brain activity from the scalp surface,which allows EEG recordings to be performed more easily on healthy humans.In this work,a technique previously used to study spatial-temporal patterns of brain activity on animal ECoG was adapted for use on EEG.The main issues are centered on solving the problems introduced by the increment on the interelectrode distance and the procedure to detect stable frames.The results showed that spatial patterns of beta and gamma activity can also be extracted from the EEG signal by using stable frames as time markers for feature extraction.This adapted technique makes it possible to take advantage of the cognitive and phenomenological awareness of a normal healthy subject.
