Onchidium pacemaker (OP) neurons show oscillatory behaviors such as beatings and periodic or chaotic discharges. Hayashi et al. proposed a set of modified Hodgkin-Huxley equations as a model of the OP neurons and succeeded to reproduce the discharge patterns. The model consists of 8 variables including those of fast and slow potasium and sodium currents. It is difficult, however, to gain mathematical insight of the model because of high dimensionality. Recently, Kepler et al. proposed a systematic method to reduce a number of variables in H-H equations without loss of essential features of the dynamics. In the present work, we apply the method by Kepler et al. to reduce the model of OP neurons by Hayashi et al. and show that the reduced one mimics well the oscallatory behaviors of the OP neurons.
The autocorrelation memory matrix is analyzed with linear algebra and the relations between the memory pattern vectors and the eigenvectors of the memory matrix are investigated. It is elucidated that increase of the memory ratio causes wider distribution of the eigenvalues, which results in loss of memory. The present paper introduces the sign alternating memorization method and shows that this method realizes the seperation of the memory space and the narrower distribution of the eigenvalues. The result of the numerical experiments shows that this method attains greater capacity and wider basin of attraction.
A Self-organized Information Retrieval System, called SIR, is being developed to contribute to a Self-organized Information Base Research Project. SIR is especially designed to be a user-friendly information retrieval system that can be used at an early stage without a database that has been specifically designed for particular information retrieval (IR) tasks. For this purpose, SIR must become intelligent by means of self-organization during use. In order to achieve these goals, SIR is designed using a connectionist information retrieval system. In this article, we describe the design philosophy and the configuration of SIR, and some experimental performance measurements.
The recent discovery of nitric oxide (NO) as an intercellular messenger has brought about surprizing impacts in many branches of medicine and life science. This article reviews such developments in neuroscience where the simple gas molecule acts as an endogenous neuro-transmitter. The basic scheme of NO-mediated neural signalling is explained, along with examples illustrating new understanding of synaptic plasticity such as long-term potentiation. Also reviewed is the enzymological principle which commonly underlies the NO functions in physiology, immunology and oncology. Possible influence of NO on neural network modelling is discussed briefly.