Concept Of Stochastic Resonance Research Articles

Research work under Visvesvaraya YFRF

This research work concentrates on developing new methodologies for weak signal detection, which are further used in the watermark detection application. The proposed detectors perform comparable to or better than most of the state-of-the-art techniques. Stochastic resonance plays a significant role in weak signal detection. Injection of precalculated noise is a big concern for us. Noises i.e., symmetric and asymmetric have been utilized for getting an improved version of the weak signal detector. With equality and non-equality constraints, we use the particle swarm optimization method to optimize the objective function. After that, we investigate a new detector based on the fractional operator. The noisy signal is convolved with the coefficients of fractional order filter. The proposed method has been tested with different standard performance parameters. The results of the proposed detector have been compared with state-of-the-art detectors such as a threshold detector (TD), neural network-based detector etc. The robustness of the proposed detector with respect to the parameters of signal and noise has also been explored. The problem of weak DC signal detection present in non-Gaussian noise is always a troublesome because of the non-linearity of the test statistic. We have used the concept of stochastic resonance in a conventional neural network-based detector. A predefined noise is injected during the back-propagation algorithm in order to minimize the error. The reduction in errors (in terms of enhancement in the probability of detection, $$P_D$$ at a constant value of the probability of false alarm, $$P_{FA}$$ ) and faster convergence give a right justification to use the concept of stochastic resonance in the neural network-based detector.

Velocity controlled pattern writing: An application of stochastic resonance.

In the present work, the concept of stochastic resonance is employed for pattern fabrication. In particular, the interplay of noise amplitudes and intrinsic system time scales is investigated. This interplay enabled us to obtain preordained patterns. Experiments were performed galvanostatically in a two electrode electrochemical cell onto a n-type Si substrate using a coherent wavelength laser source of 5 mW intensity. A focused laser beam was swept along the silicon substrate unidirectionally by moving the electrochemical cell at different velocities. By systematic tuning of the velocity, we have observed a unimodal variation in the contrast of the pattern. This indicates the occurrence of the stochastic resonance phenomena. Corresponding numerical simulations, performed on a spatial array of diffusively coupled FitzHugh-Nagumo oscillators in the presence of external noise, reveal good agreement with the experimental observations.

Dynamic origin of spike and wave discharges in the brain

Spike and wave discharges are the main electrographic characteristic of a number of epileptic brain disorders including childhood absence epilepsy and photosensitive epilepsy. The basic dynamic mechanism that underlies the occurrence of these abnormal electrical patterns in the brain is not well understood. The current paper aims to provide a dynamic explanation for features and generation mechanism of spike and wave discharges in the brain. The main proposition of this study is that epileptic seizures could be interpreted as a resonance phenomenon rather than a limit cycle behavior. To shows this, a revised version of Jansen-Rit neural mass model is employed. The system can switch between monostable and bistable regimes, which are considered in this paper as wake and sleep states of the brain, respectively. In particular, it is shown that, in monostable region, the model can depict the alpha rhythm and alpha rhythm suppression due to mental activity. Frequency responses of the model near the bistable regime demonstrate that high amplitude harmonic excitation may lead to spike and wave like oscillations. Based on the computational results and the concept of stochastic resonance, a model for absence epilepsy is presented which can simulate spontaneous transitions between ictal and interictal states. Finally, it is shown that spike and wave discharges during epileptic seizures can be explained as a resonance phenomenon in a nonlinear system.

P201 Modulating cortical dynamics of binocular rivalry with tRNS over primary visual cortex

Question We have recently shown that adding random electrical noise transcranially to the cortex can enhance the detectability of weak visual signals, a phenomenon known as stochastic resonance (SR). However, the concept of stochastic resonance is not only valid for stable systems but can also be used to influence transitions of dynamical systems that consist of multiple attractor states. Here we use binocular rivalry as a model to investigate whether peripherally or centrally added noise influences transitions between different perceptual states. Binocular rivalry is characterized by three naturally occurring attractor states with different stabilities (i.e. seeing the left eye stimulus, the right eye stimulus, or both) and by a spontaneous stochastic switching between these states. This switching is believed to be driven by a combination of neuronal adaptation and noise. Methods In a series of experiments we presented visual gratings to each eye separately using a mirror stereoscope. Subjects continuously reported whether they perceived the left eye stimulus, the right eye stimulus or a mixed percept. We tested whether switching dynamics were influenced when noise was either added peripherally on the screen or centrally by applying tRNS (100–640 Hz, 1 mA amplitude) to the visual cortex. Results and conclusions For low contrast stimuli, we found a significantly faster transition from the mixed to the single precepts when noise was added directly to the visual stimulus ( p = 0.03) or to visual cortex using tRNS ( p = 0.02). Our results show that tRNS can be used to modulate the dynamics of cortical processing.

P199 tES effects on a visual orientation discrimination task: Noise induction in a non-linear system

Recent studies have shown that the stimulation-dependent model is unable to account for the complex experimental evidence observed when applying transcranial electrical stimulation (tES) outside the motor cortex. An alternative model refers to the concept of stochastic resonance. It is based on the assumption that the injected electric field in the brain is unspecific; therefore, it can be defined as noise induction in a non-linear system (Miniussi et al., 2013). According to the model, the interaction between the injected noise and the ongoing activity of the system (in terms of signal to noise ratio, SNr) is a key factor in determining behavioral effects. The SNr in neural networks may change depending on task demands; consequently, also the response of the system to brain stimulation is expected to vary. However, up to now only few studies have considered elements which may affect the state of the system, such as performance level and interindividual variability. The present study aims to investigate whether the stochastic resonance framework can account for behavioral outcomes when an excitatory type of tES is applied (Paulus, 2011; Woods et al., 2016). 36 young healthy participants were asked to perform a visual orientation discrimination task while receiving anodal transcranial direct current stimulation or high frequency transcranial random noise stimulation (hf-tRNS) on the primary visual cortex. In Experiment 1 subjects were presented with a large range of angular differences between the reference and the target stimulus. In Experiment 2 task difficulty was manipulated according to individual performance previously recorded, in order to control for intersubjects variability. Different levels of performance were obtained in a between subjects design. Half of the subjects received sham stimulation. In Experiment 1 an increased performance was observed in the experimental group only for hf-tRNS, while for Experiment 2 no main effect of stimulation was present for any of the conditions. The present study provides evidence not sustaining the stimulation-dependent model, suggesting a more complex relationship between stimulation, brain and behavioral outcomes. Taken together, these findings stress the need of taking into account the state of the system when applying tES.

Asynchronous Digital Circuit Design using Noise-Driven Stochastic Gates

This paper presents a novel CMOS configuration of the basic logic gates using the concept of stochastic resonance (SR). In this framework, the SR effect applied to nonlinear electrical systems to build logical gates (SR gates) have been studied. These systems exhibit hysteresis; this property is crucial, because it ensures the stability of all logic states. Moreover, the application of an external bias, allows the selection of logic operation. However, in this configuration the timing is limited, which has been reviewed in this study; therefore, the most suitable applications of the SR gates are in asynchronous circuit design. The Huffman model is used to simulate the performance of a sequential circuit, designed with the SR gates. SPICE simulations are run using a 0.18-m CMOS technology. The simulations results have proven the effective performance of the SR gates for an optimal amount of noise, despite the introduction of an intentional mismatch between the threshold voltages of the transistors.

Topologically inequivalent orbits induced by noise.

In this Letter we extend the concept of stochastic resonance. We show that in forced excitable systems noise can be responsible for the appearance of recurrences presenting a robust topological organization inequivalent to the periodic orbits of the deterministic system. As in stochastic resonance, these new structures are most pronounced at an optimal noise intensity.

Stochastic resonance for quantum channels

The concept of stochastic resonance in nonlinear dynamics is applied to interpret the capacity of noisy quantum channels. The two-Pauli channel is used to illustrate the idea. The fidelity of the channel is also considered. Noise enhancement is found for the channel fidelity but not for the channel capacity.

What are stochastic filtering and stochastic resonance?

The concept of stochastic resonance (SR) was introduced in 1981 in the study of ice-age periodicity in the northern hemisphere. To describe this phenomenon, a relaxation model — an overdamped bistable oscillator — is used. SR is caused by the simultaneous action of a periodic signal and noise and appears as a nonmonotone response to noise intensity variations. Since the subject of the study is actually the filter passband width as a function of noise intensity, 'stochastic filtering' (SF) seems to be a more appropriate term to describe the phenomenon. It is shown that when driven by a signal and noise, a low-attenuation bistable oscillator also displays ordinary SR when the signal frequency coincides with the effective noise-intensity-dependent frequency of the oscillator. Thus the possibility of the resonance being controlled by varying the noise intensity arises.

Stochastic Resonance: A New Concept of Neural Coding

Stochastic resonance is a common phenomenon allowing for the detection of weak sig nals amidst substantial background noise, which, peculiarly, is a necessary component. The concept of stochastic resonance is reviewed, together with several recent studies demonstrating its role in neural systems coding. Models in which stochastic resonance is applied to sensory afferent behavior predict accurately some of the firing properties in vertebrate nerve preparations. Stochastic resonance has recently been shown to be op erant in the human PNS, in both muscle spindles and cutaneous afferents. It may offer clues as to how the nervous system sustains our fine discriminative abilities. NEURO SCIENTIST 3:211-214,1997

Theory of stochastic resonance.

The concept of stochastic resonance has been introduced previously to describe a curious phenomenon in bistable systems subject to both periodic and random forcing: an increase in the input noise can result in an improvement in the output signal-to-noise ratio. In this paper we present a detailed theoretical and numerical study of stochastic resonance, based on a rate equation approach. The main result is an equation for the output signal-to-noise ratio as a function of the rate at which noise induces hopping between the two states. The manner in which the input noise strength determines this hopping rate depends on the precise nature of the bistable system. For this reason, the theory is applied to two classes of bistable systems, the double-well (continuous) system and the two-state (discrete) system. The theory is tested in detail against digital simulations.