Glial cells include several types of cells such as astrocytes, and oligodendrocytes apart from the neurons in the brain. In particular, astrocytes are known to be important in higher brain function and are therefore sometimes simply called glial cells. An astrocyte can transmit signals to other astrocytes and neurons using ion concentrations. Thus, we expect that the functions of an astrocyte can be applied to an artificial neural network. In this study, we propose a multi-layer perceptron (MLP) with a pulse glial chain. The proposed MLP contains glia (astrocytes) in a hidden layer. The glia are connected to neurons and are excited by the outputs of the neurons. The excited glia generate pulses that affect the excitation thresholds of the neurons and their neighboring glia. The glial network provides a type of positional relationship between the neurons in the hidden layer, which can enhance the performance of MLP learning. We confirm through computer simulations that the proposed MLP has better learning performance than a conventional MLP.

This letter proposes a novel design approach for optimal contourlet filter banks based on the parametric 9/7 filter family. The Laplacian pyramid decomposition is replaced by optimal 9/7 filter banks with rational coefficients, and directional filter banks are activated using a pkva 12 filter in the contourlets. Moreover, based on this optimal 9/7 filter, we present an image denoising approach using a contourlet domain hidden Markov tree model. Finally, experimental results show that our approach in denoising images with texture detail is only 0.20 dB less compared to the method of Po and Do, and the visual quality is as good as for their method. Compared with the method of Po and Do, our approach has lower computational complexity and is more suitable for VLSI hardware implementation.

In this study, we propose a multi-layer perceptron with a glial network which is inspired from the features of glias in the brain. All glias in the proposed network generate independent oscillations, and the oscillations propagate through the glial network with attenuation. We apply the proposed network to the two-spiral problem. Computer simulations show that the proposed network gains a better performance than the conventional multi-layer perceptron.