Shin Mizutani

The Dynamics of Auditory Streaming: Psychophysics, Neuroimaging, and Modeling

Listening to a speaker or a melody in the presence of competing sounds crucially depends on our brain’s sophisticated ability to organize a complex sound mixture changing over time into coherent perceptual objects or “streams”, which generally correspond to sound sources in the environment (Bregman 1990). The acoustic factors governing this “auditory streaming” are well established (Carlyon 2004), and various theories have been proposed to explain auditory stream formation (Anstis and Saida 1985; Beauvois and Meddis 1991; Bregman 1990; Hartman and Johnson 1991; McCabe and Denham 1997; van Noorden, 1975). However, it remains unclear how and where auditory streaming is achieved in the brain. A common limitation of early studies that tried to find the neural correlates of auditory streaming is that the neural response patterns corresponding to the different states of perceptual streaming were evoked by physically different stimuli (Alain et al. 1998; Fishman et al. 2001, 2004; Naatanen et al. 2001; Sussman et al. 1999). This makes it difficult to determine whether the neural response patterns reflect perception per se or they simply reflect the physical properties of the stimuli. To overcome this difficulty, recent studies have taken advantage of the fact that the segregation of sounds into streams typically takes several seconds to build up (Carlyon et al. 2001; Cusack 2005; Gutschalk et al. 2005; Micheyl et al. 2005). Under appropriate conditions, a physically unchanging sequence of alternating tones initially tends to be heard as a single coherent stream, and after several seconds it appears to split into two distinct streams (Anstis and Saida 1985). This makes it possible to compare neural responses corresponding to different percepts without introducing any physical change in the stimulus. Based on this approach, the neural correlates of auditory streaming have been found in the primary auditory cortex (Micheyl et al. 2005), the non-primary auditory cortex (Gutchalk et al. 2005), and the intraparietal

Gate voltage control of stochastic resonance in carbon nanotube field effect transistors

On the recent developments in nano devices, carbon nanotube (CNT) is one of the candidates for next generation devices. For device applications, it might be the problem that CNTs show large noise because of large surface area. However, sometimes nonlinear systems have advantages in the working with noise. In stochastic resonance (SR), noise could enhance the working properties of devices. Therefore, we combined the noise CNT field effect transistor (FET) and the nonlinear CNT-FET as a test nonlinear system, and the sine wave amplification in the transistor with noise was measured. For the single wall CNTs, noise has the gate voltage (Vg) dependence with 1/ƒ type noise. We prepared several intensity of noise by the amplification and the gate voltage control between −4 V and −1 V for 1//ƒ noise that come from noise of CNT-FETs. Using this noise, we will discuss about the nonlinear response of CNT-FET under the controlled noise by the gate voltage.

Noise‐assisted distributed detection in sensor networks

We analyze the performance of a distributed sensor network which fuses the detection results from multiple binary detectors at a single data fusion center in the presence of noise. We show the property of noise‐assisted detection, whereby the detection correctness probability can be improved by adding noise. We point out that this property can be observed when the data fusion is not optimal, even when each detector has an optimal threshold. This result is significant from the point of view of optimizing the whole system including noise levels to optimize detection.

Noise-induced order in type-I intermittency

We study the dynamics of a pair of two uncoupled identical chaotic elements driven by common noise. When each element exhibits type-I intermittency, we observe that two uncoupled elements synchronize each other after a finite time of interval for a certain range of the noise intensity. In order to clarify the mechanism of this noise-induced synchronization phenomenon, we focus on the effect of external noise on the fluctuation of the local expansion rate of orbits to perturbations of type-I intermittency. It is found that the probability that the finite-time Liapunov exponent (FTLE) takes a negative value may increase due to the introduction of noise, whereas the Liapunov exponent itself remains to be positive. We show that this noise-induced enhancement of fluctuation may cause the synchronization and also discuss the relation between the statistical properties of relaxation process of the synchronization and the fluctuation properties of FTLE in terms of the thermodynamic formalism.

Synchronization of phase slips in chaotic map

We show that when paced chaotic oscillators, which can be flows or maps, are coupled appropriately, phase slips produced by each oscillator are synchronized. If a periodically driven chaotic oscillator strays from a phase synchronization region, the phase difference between the oscillator and the pacer jumps intermittently by 2π, which is called a phase slip. When two sinusoidally forced Roessler oscillators are coupled appropriately, phase slips produced by the two oscillators occur simultaneously, that is the phase slips are synchronized. We also show that if the coupled oscillator deviates slightly from the slip synchronization region, a portion of the simultaneous phase slips are desynchronized, namely only one of the two oscillators produces a phase slip. Such phenomena as synchronized phase slips and partially synchronized phase slips can be reproduced by a coupled map system. We investigate some statistical properties and dynamical structures of the phenomena by investigating the coupled map system.

Enhancement of deterministic stochastic resonance in coupled chaotic systems

We show that deterministic stochastic resonance (DSR) can be enhanced by coupling of chaotic oscillators. We study periodic-forced chaotic oscillators coupled to each other. One oscillator phase and periodic force phase are synchronized with each other when the force strength is larger than a critical value. When we set the force strength below the critical value, the phase synchronization occasionally fails. We can observe a quick jump in phase difference between one oscillator and periodic force. In this study, we focus on this phase slip and consider one forced chaotic oscillator as a resonator using the phase slips. When we consider coupled resonators, the coupling strength becomes a bifurcation parameter that has a critical point between asynchronous and synchronous phase slip state. Increases in coupling strength leads to a higher degree of phase slip synchronization. The coupling helps to synchronize the slips with a cooperative effect. Therefore, it can enhance the coincident response to the signal. Optimal coupling strength maximizes the resonance response. This enhancement provides some advantages for signal detection applications using DSR. It is considered that intrinsic fluctuations are important for information processing in biological system. This coupled system may be useful for a model study of neural information processing.

Deterministic stochastic resonance in chaotic diffusion

We show deterministic stochastic resonance (DSR) in chaotic diffusion when the diffusion map is modulated by a sinusoid. In chaotic diffusion, the map parameter determines the state transition rate and the diffusion coefficient. The transition rate shows the diffusion intensity. Therefore, the parameter represents the intensity of the internal fluctuation. By this fact, increase of the parameter maximizes the response of DSR as in standard stochastic resonance (SR) where the external noise intensity optimizes the response. Sinusoidally modulated diffusion is regarded as a stochastic process whose transition rate is modulated by the sinusoid. Therefore, the transition dynamics can be approximated by a time-dependent random walk process. Using the mean transition rate function against the map parameter, we can derive the DSR response depending on the parameter. Our approach is based on the rate modulation theory for SR. Even when the diffusion map is modulated by the sinusoid and noise from an external environment, the increasing parameter can also maximize the DSR response. We can calculate the DSR response depending on the external noise intensity and the map parameter. DSR takes advantage of applications to signal detection because the system has the control parameter corresponding to the internal fluctuation intensity.

Noise‐assisted quantization in sensor networks

We propose the use of sensing noise as a practical way to introduce heterogeneity in a homogeneous sensing network, and thus improve sensing performance. In particular, we show that in a sensor network which aggregates the results from multi‐level quantizing sensors each having the same input signal, adding independent noise to the sensors can reduce the error between the input signal and output obtained by simply combining the outputs from all the sensors. We can show that this noise‐assisted quantization always occurs when the quantizers of the sensors have identical sets of thresholds. This condition is suboptimal as a quantizer system. We also show the case of the optimal quantizer system in which the error monotonically with the noise intensity.