Haixian Zhang

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Researches on neural population coding have revealed that continuous stimuli, such as orientation, moving direction, and the spatial location of objects could be encoded as continuous attractors in neural networks. The dynamical behaviors of continuous attractors are interesting properties of recurrent neural networks. This paper proposes a class of(More)
In the present study, post inflammation irritable bowel syndrome (PI-IBS) rats were firstly established by intracolonic instillation of acetic acid with restraint stress. Then the pharmacokinetics of berberine in the rat plasma were compared after oral administration of berberine hydrochloride (25 mg/kg) to normal rats and PI-IBS rats. Quantification of(More)
This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs(More)
Least squares support vector machine (LS-SVM) is a modified version of traditional support vector machine (SVM). LS-SVM considers equality constraints, therefore it solves a set of linear equations instead of quadratic programming problem in SVM. However, the sparseness of LS-SVM is lost due to itpsilas isin-sensitive cost function. Sparseness can be(More)
We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces. The proposed technique can reveal the membership of multiple subspaces through the self-expressiveness property of the data. In particular, the SLRR method considers a collaborative(More)
Regularized linear regression based representation techniques for face recognition (FR) have attracted a lot of attention in past years. The l<sub>1</sub>-regularized sparse representation based classification (SRC) method achieves state-of-the-art results in FR. However, recently several studies have shown the role of collaborative representation (CR) that(More)
Manifold learning (ML) is a research topic of great interest in the field of machine learning that aims to determine the appropriate low-dimensional embeddings of data. The embeddings should preserve the intrinsic structure of the data manifold. Many ML techniques have been proposed to learn the underlying manifold of data. It is crucial to effectively(More)
Continuous attractors of recurrent neural networks (RNNs) have attracted extensive interests in recent years. It is often used to describe the encoding of continuous stimuli such as orientation, moving direction and spatial location of objects. This paper studies the dynamic shift mechanism of a class of continuous attractor neural networks. It shows that(More)