Recent developments in engineering applications of stochastic resonance have expanded to various fields, especially biomedicine. Deterministic chaos generates a phenomenon known as chaotic resonance, which is similar to stochastic resonance. However, engineering applications of chaotic resonance are limited owing to the problems in controlling chaos, despite its uniquely high sensitivity to weak signal responses. To tackle these problems, a previous study proposed “reduced region of orbit” (RRO) feedback methods, which cause chaotic resonance using external feedback signals. However, this evaluation was conducted under noise-free conditions. In actual environments, background noise and measurement errors are inevitable in the estimation of RRO feedback strength; therefore, their impact must be elucidated for the application of RRO feedback methods. In this study, we evaluated the chaotic resonance induced by the RRO feedback method in chaotic neural systems in the presence of stochastic noise. Specifically, we focused on the chaotic resonance induced by RRO feedback signals in a neural system composed of excitatory and inhibitory neurons, a typical neural system wherein chaotic resonance is observed in the presence of additive noise and feedback signals including the measurement error (called contaminant noise). It was found that for a relatively small noise strength, both types of noise commonly degenerated the degree of synchronization in chaotic resonance induced by RRO feedback signals, although these characteristics were significantly different. In contrast, chaos-chaos intermittency synchronization was observed for a relatively high noise strength owing to the noise-induced attractor merging bifurcation for both types of noise. In practical neural systems, the influence of noise is unavoidable; therefore, this study highlighted the importance of the countermeasures for noise in the application of chaotic resonance and utilization of noise-induced attractor merging bifurcation.

Stochastic resonance can improve the signal-to-noise ratio of digital baseband signals. However, the output of SR system needs some time for evolution to achieve global steady-state. This paper first analyzes the evolution time of SR systems, which is an important factor for digital baseband signal processing based on SR. This investigation shows that the sampling number per symbol should be rather large, and the minimum sampling number per symbol is deduced according to the evolution time of SR system.

The phenomenon of stochastic resonance (SR) in a mono-threshold-system-based detector (MTD) with additive background noise and multiplicative external noise is investigated. On the basis of maximum a posteriori probability (MAP) criterion, we deal with the binary signal transmission in four scenarios. The performance of the MTD is characterized by the probability of error detection, and the effects of system threshold and noise intensity on detectability are discussed in this paper. Similar to prior studies that focus on additive noises, along with increases in noise intensity, we also observe a non-monotone phenomenon in the multiplicative ways. However, unlike the case with the additive noise, optimal multiplicative noises all tend toward infinity for fixed additive noise intensities. The results of our model are potentially useful for the design of a sensor network and can help one to understand the biological mechanism of synaptic transmission.

A power allocation to active and reactive power in stochastic resonance is discussed for energy harvesting from noise. It is confirmed that active power can be increased at stochastic resonance, in the same way of the relationship between energy and phase at an appropriate setting in resonance.

This paper introduces several techniques for achieving RF and analog CMOS circuits for wireless communication systems under ultra-low-voltage supply, such as 0.5 V. Forward body biasing and inverter-based circuit techniques were applied in the design of a feedforward Δ-ΣA/D modulator operating with a 0.5 V supply. Transformer utilization is also presented as an inductor area reduction technique. In addition, application of stochastic resonance to A/D conversion is discussed as a future technology.

We found a new class of stochastic resonance (SR) in a simple neural network that consists of i) photoreceptors generating nonuniform outputs for common inputs with random offsets, ii) an ensemble of noisy McCulloch-Pitts (MP) neurons each of which has random threshold values in the temporal domain, iii) local coupling connections between the photoreceptors and the MP neurons with variable receptive fields (RFs), iv) output cells, and v) local coupling connections between the MP neurons and the output cells. We calculated correlation values between the inputs and the outputs as a function of the RF size and intensities of the random components in photoreceptors and the MP neurons. We show the existence of "optimal noise intensities" of the MP neurons under the nonidentical photoreceptors and "nonzero optimal RF sizes," which indicated that optimal correlation values of this SR model were determined by two critical parameters; noise intensities (well-known) and RF sizes as a new parameter.

A signal detection system using noise statistical processing is proposed. By approaching the problems of low voltage and high noise from miniaturization of a device from a stochastic point of view, a faint-signal receiving system that can effectively detect subthreshold and noise level signals has been developed. In addition, an alternative to statistical processing is proposed, and would be successfully implemented on a circuit. For the proposed signal detection method, the detection sensitivity was investigated using numerical simulation, and the detection sensitivity was sufficiently high to detect even a signal with a signal-to-inherent-noise ratio of -14 dB. Thus, it is anticipated that the application of this system to an integrated circuit will have a significant impact on signal processing.

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.

Coherence resonance in propagating spikes generated by noise in spatially distributed excitable media is studied with computer simulation and circuit experiment on the FitzHugh-Nagumo model. White noise is added to the one end of the media to generate spikes, which propagate to the other end. The mean and standard deviation of the interspike intervals of the spikes after propagation take minimum values at the intermediate strength of the added noise. This shows stronger coherence than obtained in the previous studies.

Enhancement of resonance is shown by coupling and summing in sinusoidally driven chaotic neural networks. This resonance phenomenon has a peak at a drive frequency similar to noise-induced stochastic resonance (SR), however, the mechanism is different from noise-induced SR. We numerically study the properties of resonance in chaotic neural networks in the turbulent phase with summing and homogeneous coupling, with particular consideration of enhancement of the signal-to-noise ratio (SNR) by coupling and summing. Summing networks can enhance the SNR of a mean field based on the law of large numbers. Global coupling can enhance the SNR of a mean field and a neuron in the network. However, enhancement is not guaranteed and depends on the parameters. A combination of coupling and summing enhances the SNR, but summing to provide a mean field is more effective than coupling on a neuron level to promote the SNR. The global coupling network has a negative correlation between the SNR of the mean field and the Kolmogorov-Sinai (KS) entropy, and between the SNR of a neuron in the network and the KS entropy. This negative correlation is similar to the results of the driven single neuron model. The SNR is saturated as an increase in the drive amplitude, and further increases change the state into a nonchaotic one. The SNR is enhanced around a few frequencies and the dependence on frequency is clearer and smoother than the results of the driven single neuron model. Such dependence on the drive amplitude and frequency exhibits similarities to the results of the driven single neuron model. The nearest neighbor coupling network with a periodic or free boundary can also enhance the SNR of a neuron depending on the parameters. The network also has a negative correlation between the SNR of a neuron and the KS entropy whenever the boundary is periodic or free. The network with a free boundary does not have a significant effect on the SNR from both edges of the free boundaries.

We show by numerical calculations that a chaotic neuron model driven by a weak sinusoid has resonance. This resonance phenomenon has a peak at a drive frequency similar to that of noise-induced stochastic resonance (SR). This neuron model was proposed from biological studies and shows a chaotic response when a parameter is varied. SR is a noise induced effect in driven nonlinear dynamical systems. The basic SR mechanism can be understood through synchronization and resonance in a bistable system driven by a subthreshold sinusoid plus noise. Therefore, background noise can boost a weak signal using SR. This effect is found in biological sensory neurons and obviously has some useful sensory function. The signal-to-noise ratio (SNR) of the driven chaotic neuron model is improved depending on the drive frequency; especially at low frequencies, the SNR is remarkably promoted. The resonance mechanism in the model is different from the noise-induced SR mechanism. This paper considers the mechanism and proposes possible explanations. Also, the meaning of chaos in biological systems based on the resonance phenomenon is considered.