Based on the similarity between the neural and the immune networks, a modified PDP model is adopted to simulate the idiotype networks of the immune system. Major modifications peculiar to immune networks are the introduction of time delays and the bilateral and asymmetric interaction between units. Simulation results show nonlinear behaviors of networks such as hard oscillations and chaos. The behaviors are closely related to the structure of networks. It is suggested that the analysis of the immune system can inspire new scheme in parallel distributed information processing.

In this paper, a robust cyclic ADC architecture with β-encoder is proposed and circuit scheme using switched-capacitor (SC) circuit is introduced. Different from the conventional binary ADC, the redundancy of proposed cyclic ADC outputs β-expansion code and has an advantage of error correction. This feature makes ADC robust against the offset of comparator capacitor mismatch and finite DC gain of amplifier in multiplying-DAC (MDAC). Because the power penalty of high-gain wideband amplifier and the required accuracy of circuit elements for high resolution ADC can be relaxed, the proposed architecture is suitable for deep submicron CMOS technologies beyond 90 nm. We also propose a β-value estimation algorithm to realize high accuracy ADC based on β-expansion. The simulation results show the effectiveness of proposed architecture and robustness of β-encoder.

An asynchronous pulse neural network model which is suitable for VLSI implementation is proposed. The model neuron can function as a coincidence detector as well as an integrator depending on its internal time-constant relative to the external one, and show complex dynamical behavior including chaotic responses. A network with the proposed neurons can process spatio-temporal coded information through dynamical cell assemblies with functional synaptic connections.

Deterministic nonlinear prediction is applied to both artificial and real time series data in order to investigate orbital-instabilities, short-term predictabilities and long-term unpredictabilities, which are important characteristics of deterministic chaos. As an example of artificial data, bimodal maps of chaotic neuron models are approximated by radial basis function networks, and the approximation abilities are evaluated by applying deterministic nonlinear prediction, estimating Lyapunov exponents and reconstructing bifurcation diagrams of chaotic neuron models. The functional approximation is also applied to squid giant axon response as an example of real data. Two metnods, the standard and smoothing interpolation, are adopted to construct radial basis function networks; while the former is the conventional method that reproduces data points strictly, the latter considers both faithfulness and smoothness of interpolation which is suitable under existence of noise. In order to take a balance between faithfulness and smoothness of interpolation, cross validation is applied to obtain an optimal one. As a result, it is confirmed that by the smoothing interpolation prediction performances are very high and estimated Lyapunov exponents are very similar to actual ones, even though in the case of periodic responses. Moreover, it is confirmed that reconstructed bifurcation diagrams are very similar to the original ones.

We propose a novel segmentation algorithm which combines an image segmentation method into small regions with chaotic neurodynamics that has already been clarified to be effective for solving some combinatorial optimization problems. The basic algorithm of an image segmentation is the variable-shape-bloch-segmentation (VB) which searches an opti-mal state of the segmentation by moving the vertices of quadran-gular regions. However, since the algorithm for moving vertices is based upon steepest descent dynamics, this segmentation method has a local minimum problem that the algorithm gets stuck at undesirable local minima. In order to treat such a problem of the VB and improve its performance, we introduce chaotic neurodynamics for optimization. The results of our novel method are compared with those of conventional stochastic dynamics for escaping from undesirable local minima. As a result, the better results are obtained with the chaotic neurodynamical image segmentation.

We analyse a mathematical neuron model with chaotic dynamics, or a chaotic neuron model by the generalized dimensions and the f(α) spectrum. The results show that the multi-fractal structure of a chaotic neuron model can be quantified by the f(α) spectrum.

This paper presents an improved current-mode circuit for implementation of a chaotic neuron model. The proposed circuit uses a switched-current integrator and a nonlinear output function circuit, which is based on an operational transconductance amplifier, as building blocks. Is is shown by SPICE simulations and experiments using discrete elements that the proposed circuit well replicates the behavior of the chaotic neuron model.

Though response of neurons is mainly decided by synaptic events, the length of a time window for the neuronal response has still not been clarified. In this paper, we analyse the time window within which a neuron processes synaptic events, on the basis of the Hodgkin-Huxley equations. Our simulation shows that an active membrane property makes neurons' behavior complex, and that a few milliseconds is plausible as the time window. A neuron seems to detect coincidence synaptic events in such a time window.

Recent physiological studies on synaptic plasticity have shown that synaptic weights change depending on fine timing of presynaptic and postsynaptic spikes. Here, we show that a phenomenon similar to stochastic resonance with respect to background noise is observed on spike-timing dependent synaptic plasticity (STDP) that can contribute to stable propagation of precisely timed spikes in a multi-layered feedforward neural network.

Recently, new optimization machines based on non-silicon physical systems, such as quantum annealing machines, have been developed, and their commercialization has been started. These machines solve the problems by searching the state of the Ising spins, which minimizes the Ising Hamiltonian. Such a property of minimization of the Ising Hamiltonian can be applied to various combinatorial optimization problems. In this paper, we introduce the coherent Ising machine (CIM), which can solve the problems in a milli-second order, and has higher performance than the quantum annealing machines especially on the problems with dense mutual connections in the corresponding Ising model. We explain how a target problem can be implemented on the CIM, based on the optimization scheme using the mutually connected neural networks. We apply the CIM to traveling salesman problems as an example benchmark, and show experimental results of the real machine of the CIM. We also apply the CIM to several combinatorial optimization problems in wireless communication systems, such as channel assignment problems. The CIM's ultra-fast optimization may enable a real-time optimization of various communication systems even in a dynamic communication environment.