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The image matching is an important technique in the image processing and the method using Pulse Coupled Neural Network (PCNN) had been proposed. One of the useful feature of the method is that the method is valid for the image matching among rotated, magnified and shrunk images. We have been proposed the parameter learning method of the PCNN for the image matching. Considering that the image matching technique will utilize for any advanced image processing such as a content based image retrieval, the capacity and the versatility of the method are important characteristics to evaluate the method. In this study, our method is tested using total 17,920 images and we describe the characteristics of the capacity and the versatility of image matching method using PCNN with our parameter learning algorithm.

A technique for the image recognition is major issue in the image processing and the image recognition method using pulse coupled neural network (PCNN) have been studied as one of the valid method. The most outstanding feature of the method using PCNN is that the method is valid for the rotation, magnification and shrinking of the image. Also, the good compatibility to the hardware implementation is significant feature of the PCNN. In our previous study, we proposed the GA based learning method for the PCNN parameters which enable the reliable results of image recognition. In this study, we evaluate the image recognition method using PCNN with our learning method. In the simulation results, we clarify the characteristics of recognition rate to the number of the images to be learned using our proposed learning method.

The Pulse Coupled Neural Network (PCNN) had been proposed as a model of visual cortex and a lot of applications to the image processing have been proposed recently. Authors also have been proposed Inhibitory Connected PCNN (IC-PCNN) which shows good performances for the color image processing. In our recent study, we had been shown that the IC-PCNN can obtain successful results for the color image segmentation. In this study, we show the effect of the inhibitory connections to the characteristics of synchronous firing assembly. Here we consider that the results will be a key to find appropriate values of inhibitory connections for the image processing using IC-PCNN. In simulations, we show that the valid domains of inhibitory connections for the color image segmentation exists.

The pattern recognition using Pulse Coupled Neural Network (PCNN) had been proposed. In conventional studies, the parameters in the PCNN are used to be defined empirically and the optimization of parameters has been known as a remaining problem of PCNN. In this study, we show a method to apply the real coded genetic algorithm to the parameter optimization of the PCNN and we also show performances of pattern recognition by the PCNN with learned parameters.

A Pulse Coupled Neural Network (PCNN) is proposed as a numerical model of cat visual cortex, and it has been applied to the engineering fields especially in an image processing, e.g., segmentation, edge enhancement, and so on. The PCNN model consists of neurons with two kind of inputs, namely feeding input and linking input and they each have a lot of parameters. The Parameters are used to be defined empirically and the optimization of parameters has been known as one of the remaining problem of PCNN. According to the recent studies, parameters in PCNN will be able to be given using parameter learning rule or evolutionary programming. However these methods require teaching images for the learning. In this study, we propose a parameter adjustment method of PCNN for the image segmentation. The proposed method changes the parameters through the iterations of trial of segmentation and the method doesn't require any teaching signal or teaching pattern. The successful results are obtained in the simulations, and we conclude that the proposed method shows good performance for the parameter adjustment of PCNNs.

A Pulse Coupled Neural Network (PCNN) is a kind of numerical model of cat visual cortex and it can explain synchronous dynamics of neurons' activity in the visual cortex. On the other hand, as an engineering application, it is shown that the PCNN can applied to the image processing, e.g. segmentation, edge enhancement, and so on. The PCNN model consists of neurons and two kind of inputs, namely feeding inputs and linking inputs with leaky integrators. These inputs lead to discrete time evolution of its internal state and neurons generate spike output according to the internal state. The linking and feeding inputs are received from the neurons' receptive field which is defined by excitatory synaptic weights. In this study, we propose a PCNN with inhibitory connections and describe an application to a color image segmentation. In proposed model, inhibitory connections are defined by negative synaptic weights among specific neurons which detect RGB component of particular pixel of the image. Simulation results show successful results for the color image segmentation.

This paper proposes the local connected neural oscillator network which is useful for image processing. In this proposed network, each neural oscillator employs a learning method to control its phase and frequency. Since the learning method has an ability to control the phase adjustment of each neural oscillator, the information expression in the phase space is achievable. Furthermore, it is considered that the network has the ability to achieve real time image processing and information processing in a time series. The learning method is possible under the assumption that synapses have plasticity. However, since it is supposed that only the feedback synapse has plasticity, we can construct a network with high simplicity.