Stochastic resonance (SR) is given to a phenomenon that is manifest in nonlinear systems whereby generally feeble input information can be amplified and optimized by the assistance of noise. First, the basic concepts and the characteristic quantities of SR are introduced in this paper. Second, SR in biological system and its applications are reviewed in detail. At last, a summary is presented and the future researches on SR are prospected.
Animals ; Biology ; trends ; Humans ; Models, Theoretical ; Nonlinear Dynamics ; Stochastic Processes

Cross-modal stochastic resonance is a ubiquitous phenomenon, that is, a weak signal from one sensory pathway can be enhanced by the noise from a different sensory pathway. It is a special multisensory integration (MI) that can not be explained by the inverse-effectiveness rule. According to cross-modal stochastic resonance, the detection of signal is an inverted U-like function of the intensity of noise at different levels. In this paper, we reviewed the research of cross-modal stochastic resonance and put forward some possible explanations for it. These efforts raise a new idea for neural encoding and information processing of the brain.
Acoustic Stimulation ; Auditory Perception ; physiology ; Brain ; physiology ; Humans ; Mental Processes ; physiology ; Sensory Thresholds ; physiology ; Stochastic Processes ; Visual Perception ; physiology

Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.
Action Potentials/physiology* ; Computer Simulation ; Models, Neurological ; Stochastic Processes ; Neurons/physiology* ; Electromagnetic Phenomena

The somatic epigenome can be reprogrammed to a pluripotent state by a combination of transcription factors. Altering cell fate involves transcription factors cooperation, epigenetic reconfiguration, such as DNA methylation and histone modification, posttranscriptional regulation by microRNAs, and so on. Nevertheless, such reprogramming is inefficient. Evidence suggests that during the early stage of reprogramming, the process is stochastic, but by the late stage, it is deterministic. In addition to conventional reprogramming methods, dozens of small molecules have been identified that can functionally replace reprogramming factors and significantly improve induced pluripotent stem cell (iPSC) reprogramming. Indeed, iPS cells have been created recently using chemical compounds only. iPSCs are thought to display subtle genetic and epigenetic variability; this variability is not random, but occurs at hotspots across the genome. Here we discuss the progress and current perspectives in the field. Research into the reprogramming process today will pave the way for great advances in regenerative medicine in the future.
Animals ; Cell Differentiation ; Cellular Reprogramming ; MicroRNAs ; genetics ; Models, Biological ; Pluripotent Stem Cells ; cytology ; metabolism ; Stochastic Processes

The spectrogram of Doppler ultrasound signal has been widely used in clinical diagnosis. The additional frequency components arising from internal or external noise to the system will produce adverse effects on its subjective and quantitative analysis. A novel approach based on the adapted local cosine transform and the non-negative Garrote thresholding method was proposed to remove noise from quadrature Doppler signal. At first, the directional information was extracted from the quadrature signal. And then the denoising method based on the adapted local cosine transform is performed on the forward and backward flow signals, respectively. At last, the estimated signal was reconstructed from the denoised signals using Hilbert transform. In the simulation study, both the mean frequency and spectral width waveform were studied for the denoised signal. The simulation results had shown that this approach was superior to that based on the wavelet transform, especially under low SNR conditions.
Algorithms ; Artifacts ; Computer Simulation ; Fourier Analysis ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted ; Stochastic Processes ; Ultrasonics

Electrocardiographic (ECG) signal are a kind of basic physiological signals of human body, and are very important in clinical diagnosis. But the ECG signals from body surface are often interfered by noises such as 50 Hz noise, baseline displacemant, electromyography (EMG) noise and edv. These noises bring obstacle to the diagnosis of cardiovascular diseases. To eliminate the ECG signals noises mentioned above,this paper adopts LMS adaptive algorithm and wavelet transform theory to design three kinds of digital adaptive filters-adaptive noise cancellation filter, wavelet transform filter and adaptive signal dividing filter to filter the corresponding noises. The results show that the three kinds of noises existing in the ECG signal have been efficiently eliminated.
Algorithms ; Electrocardiography ; Fourier Analysis ; Humans ; Signal Processing, Computer-Assisted ; instrumentation ; Stochastic Processes ; Time Factors

In nonlinear systems, noise can improve the responses of the systems with appropriate noise intensity. This phenomenon is called stochastic resonance. Biological neural systems are noisy and stochastic resonance has been found in them experimentally and theoretically. Now many researches focus on the signal transmission and processing in neural models. So this paper introduces the researches of stochastic resonance in noisy neural models. Then the recent research achievement and progress are reviewed in the following three aspects: noise; the development of stochastic resonance; and neural network. At last, the foreground of the study is discussed.
Humans ; Models, Neurological ; Neurons ; physiology ; Nonlinear Dynamics ; Signal Transduction ; Stochastic Processes

Chaos and fractals are ubiquitous phenomena of nature. A system with fractal structure usually behaves chaos. As a complicated nonlinear dynamics system, heart has fractals structure and behaves as chaos. The deeper inherent mechanism of heart can be opened out when the chaos and fractals theory is utilized in the research of the electrical activity of heart. Generally a time series of a system was used for describing the status of the strange attractor of the system. The indices include Poincare plot, fractals dimension, Lyapunov exponent, entropy, scaling exponent, Hurst index and so on. In this article, the basic concepts and the methods of chaos and fractals were introduced firstly. Then the applications of chaos and fractals theories in the study of electrocardial signal were expounded with example of how they are used for ventricular fibrillation.
Electrocardiography ; methods ; Fractals ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted ; Stochastic Processes

A stochastic model of intracellular calcium oscillations is put forward by taking into account the random opening-closing of Ca2+ channels in endoplasmic reticulum (ER) membrane. The numerical results of the stochastic model show simple and complex calcium oscillations, which accord with the experiment results.
Calcium Channels/*metabolism ; *Calcium Signaling ; Endoplasmic Reticulum/*metabolism ; Intracellular Space ; Kinetics ; Mathematics ; Models, Biological ; Stochastic Processes

We have investigated the effects of high frequency (HF) signal on firing activity in a biologically realistic system--the noisy Hodgkin-Huxley (HH) neuron model via numerical simulations. The results show that when the HF amplitude to frequency ratio (AFR) increases, the firing rate is diminished and stochastic resonance disappears, even the HH neuron model is processing a stimulus of its most sensitive frequency. When the noise intensity is strong, the vibration resonance can be observed. Moreover, the fluctuation around the resting potential will be replaced by an oscillation of the same high frequency with the increasing AFR. The inhibition of the firing activity is consistent with the results of experiment in vivo that HF current can stop the transmission of action potential in peripheral nerve. This study is of functional significance to the biomedical research on the damages caused by electro-pollution in vivo and signal processing.
Action Potentials ; Artifacts ; Computer Simulation ; Humans ; Models, Neurological ; Neurons ; physiology ; Stochastic Processes