Showing papers by "Liqing Zhang published in 1999"

Induction of P-glycoprotein expression in astrocytes following intracerebroventricular kainate injections.

Abstract: The expression of P-glycoprotein (PGP) was studied by immunocytochemistry and light and electron microscopy, in normal rats and after intracerebroventricular kainate injections. Two antibodies to PGP, mdr (Ab-1) and c-219, were used. As in previous studies (Thiebault et al. and Jette et al.), labelled capillaries were observed in normal rats. Kainate injections resulted in death of pyramidal neurons in the hippocampus, and a proliferation of glial cells in the affected cornu ammonis fields. An increase in PGP expression was observed in reactive astrocytes as early as 1 day postinjection. Immunoreactivity peaked at 2 weeks postinjection, but was still visible as late as 10 weeks postinjection. Similar results were observed using the two antibodies. Double immunolabelling and confocal microscopy also showed that PGP was colocalised with GFAP, a marker for astrocytes. The expression of PGP in astrocytes was confirmed by electron microscopy, which showed immunoreaction product in cells containing dense bundles of glial filaments and features of reactive astrocytes. The increased PGP expression in reactive astrocytes could be part of a cellular stress response program in these cells.

Natural gradient algorithm for blind separation of overdetermined mixture with additive noise

Abstract: We study the natural gradient approach to blind separation of overdetermined mixtures. First we introduce a Lie group on the manifold of overdetermined mixtures, and endow a Riemannian metric on the manifold based on the property of the Lie group. Then we derive the natural gradient on the manifold using the isometry of the Riemannian metric. Using the natural gradient, we present a new learning algorithm based on the minimization of mutual information.

Multichannel blind deconvolution of non-minimum phase systems using information backpropagation

Abstract: We present a novel method-filter decomposition approach, for multichannel blind deconvolution of non-minimum phase systems. In earlier work we developed an efficient natural gradient algorithm for causal FIR filters. In this paper we further study the natural gradient method for noncausal filters. We decompose the doubly finite filters into a product of two filters, a noncausal FIR filter and a causal FIR filter. The natural gradient algorithm is employed to train the causal FIR filter, and a novel information backpropagation algorithm is developed for training the noncausal FIR filter. Simulations are given to illustrate the effectiveness and validity of the algorithm.

Geometrical structures of FIR manifold and their application to multichannel blind deconvolution

Abstract: We study geometrical structures on the manifold of FIR filters and their application to multichannel blind deconvolution. First we introduce the Lie group and Riemannian metric to the manifold of FIR filters. Then we derive the natural gradient on the manifold using the isometry of the Riemannian metric. Using the natural gradient, we present a novel learning algorithm for blind deconvolution based on the minimization of mutual information. We also study properties of the learning algorithm, such as equivariance and stability. Simulations are given to illustrate the effectiveness and validity of the proposed algorithm.

Blind separation and filtering using state space models

Abstract: Blind signal processing, especially independent component analysis (ICA) and multichannel blind deconvolution/equalization (MBD) problems have recently gained much interest due to many applications, especially in processing of biomedical signals (e.g. EEG, MEG, EMG, EOG, ECG), in wireless communications, 'cocktail party' problem, speech enhancement, geophysics, and source localization. In this paper both linear and nonlinear state space models for blind and semi-blind separation of linearly/nonlinearly mixed and filtered independent source signals are proposed. New unsupervised adaptive learning algorithms performing mutual independence of output signals are developed. For nonlinear mixture hyper radial basis function (HRBF), neural network proposed by Poggio and Girosi is employed and associated supervised-unsupervised learning rules for its parameters are developed.

Adaptive multichannel blind deconvolution using state-space models

Abstract: Independent component analysis (ICA) and related problems of blind source separation (BSS) and multichannel blind deconvolution (MBD) problems have recently gained much interest due to many applications in biomedical signal processing, wireless communications and geophysics. In this paper both linear and nonlinear state space models for blind and semi-blind deconvolution are proposed. New unsupervised adaptive learning algorithms performing extended linear multichannel blind deconvolution are developed. For a nonlinear mixture, a hyper radial basis function (HRBF) neural network is employed and associated supervised-unsupervised learning rules for its parameters are developed. Computer simulation experiments confirm the validity and performance of the developed models and associated learning algorithms.

Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems

Abstract: In this paper we discuss the semi parametric statistical model for blind deconvolution. First we introduce a Lie Group to the manifold of noncausal FIR filters. Then blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. A natural gradient learning algorithm is developed for training noncausal filters. Stability of the natural gradient algorithm is also analyzed in this framework.