Application Of White Noise Research Articles

Effects of white noise in walking on walking time, state anxiety, and fear of falling among the elderly with mild dementia.

ObjectiveThis study aimed to analyze the effects of white noise in walking on the walking time, state anxiety, and fear of falling of the elderly with mild dementia.MethodsSubjects were 32 elderlies with mild dementia, and they divided into experimental group and control group, respectively. In the experimental group, walking program with white noise was applied 3 times a week for 4 weeks. White noise was provided by white noise generator in walking program. In the control group, walking program only was applied. To measure the effect of white noise in walking among the subjects, the walking time, state anxiety, and fear of falling were measured. Walking time was measured by Timed Up and Go test. State anxiety related in walking was measured by Korean version of State‐Trait Anxiety Inventory. Fear of falling was used by Korean Falls Efficacy Scale.ResultsThe results of walking time showed the increase in both groups, but the statistically significant difference was not shown. However, the results of state anxiety and fear of falling showed decrease and the statistically significant difference was shown (p < .01). In comparative analysis, the statistically significant difference in the results of gate velocity between groups was not shown. However, in the results of state anxiety and fear of falling the statistically significant difference between groups was shown (p < .01).ConclusionsWhite noise in walking should be induced positively to decrease the state anxiety and fear of falling in walking among elderly with mild dementia. Thus, in their environment, to decrease of state anxiety and fear of falling occurring in walking, the application of white noise in walking situation should be considered to apply for them.

Spike-timing-dependent plasticity and subcortical waves enhance alpha oscillations in a computer model of neocortex

Event Abstract Back to Event Spike-timing-dependent plasticity and subcortical waves enhance alpha oscillations in a computer model of neocortex Samuel Neymotin1*, Cliff Kerr2, George Chadderdon3, Christopher Fietkiewicz3 and William Lytton4 1 NYU-Poly, United States 2 U Sydney, Australia 3 SUNY Downstate, United States 4 Kings County Hospital, United States A prominent alpha oscillation is a salient part of neocortical activity that emerges over the first several years of life. In retina and other tissues slow oscillations are generated during development. We hypothesized that such waves in lower centers could train the cortex to produce alpha. We here show that frequencies of this range could emerge in the network via a process of spike-timing-dependent plasticity (STDP). We used a previously published model consisting of 470 event-based integrate-and-fire cells (Neymotin et al., 2011). Excitatory and inhibitory cells were arranged in 13 types to reflect the laminar structure of sensory cortex. Each cell received external driving Poisson inputs, as well as inputs from other cells in the model, via AMPA, NMDA, and GABAA synapses. STDP was implemented at AMPA synapses, with a maximal spike-time difference of 40 ms and a decay time constant of 10 ms. After learning, we evaluated the spectral power in the network with application of white noise, to see if it had learned to produce activity in the trained bands, effectively retaining a memory of the training. The waves from lower centers were simulated by rhythmic inputs to layer 4 excitatory cells to simulate inputs via thalamus. At baseline, prior to STDP, the excitatory cell multiunit activity vector (MUA) power spectrum had a low-amplitude peak near 7 Hz and gradual attenuation towards higher frequencies (Fig. 1, orange). When STDP was implemented only at synapses on excitatory cells, the network was prone to epileptic activity, including high-frequency oscillations and pathologically high firing rates. To provide balanced inhibition in the network, we subsequently implemented STDP for excitatory synapses on inhibitory cells. STDP caused a shift in the distribution of synaptic weights, with the strength of AMPA synapses on inhibitory cells increasing more than those on excitatory cells, preventing epileptic activity from emerging. After applying STDP with no training signal, the spectral peak was reduced (Fig. 1, black) due to the added inhibition in the network. In order to train the network, we provided a subcortical training input consisting of shocks applied at several different frequencies. When a training signal of 7 Hz was applied, the activity of layer 4 excitatory cells increased, causing an increase in the spectral power. The post-training spectral peak in response to white noise was broad (7 - 22 Hz) and changed little from baseline (Fig. 1, red). A small increase in training signal rate to 8 Hz (Fig. 1, blue) produced a large increase in resultant overall spectral power. In this case, a sharp spectral peak emerged near 12.5 Hz, considerably higher than the training frequency. A smaller peak was produced at 8 Hz. Applying a 10 Hz training signal enhanced the amplitude of the spectral peak further and pushed the resultant peak upwards to form a broad profile that peaked at approximately 15 Hz (Fig. 1, green). The greater response to 8 and 10 Hz appeared to be associated with far greater spread of excitation throughout the initial, untrained network, suggesting that a resonant response can occur at these higher frequencies due to the continuation of activations for these time periods. Our model suggests that learning rates should be properly balanced between excitatory to excitatory and excitatory to inhibitory synapses in order to avoid a transition to epileptic dynamics. Alterations in excitatory weights are necessary for learned dynamics, but learning must be modulated by interneurons. Finally, we predict that alpha/beta oscillations could emerge via STDP learning of training signals projected from subcortical areas. Supported by: DARPA grant N66001-10-C-2008

A spin laser? The persistent precessing domain in superfluid [formula omitted]-B at ultralow temperatures

We discuss various properties of the persistent precessing domain (PPD, formerly PIS) in superfluid 3 He -B. These exotic NMR domains can only be excited at ultralow temperatures. At the lowest temperatures they freely precess for periods up to 2000 s . We identify various behaviors depending on the texture and magnetic field profile. We can create the domains with small generating pulses or excite them continuously but only with an excitation frequency higher than that of the domain precession. Remarkably, we can excite and sustain the precession by the application of white noise. Thus, unusually, the coherent domain can extract energy from an incoherent energy source.

Application of pseudo-random signals and cross-correlation techniques in electroanalytical chemistry

Summary The significance of application of white noise and cross-correlation function techniques in electroanalytical chemistry is described. The simplest pseudo-random signal generator and the correlator which avoid the use of large memory are constructed and applied to a few electrode systems. The responses obtained and their frequency profiles agree well with theoretical predictions.

Simulation of stationary stochastic processes

A ‘spectral-factorisation’ procedure involving the solution of a Riccati matrix differential equation is considered to determine systems which, with white-noise input signals, may be used in the simulation of stochastic processes having prescribed stationary covariances. More spe$itically, the specification of a system is made so that the covariance of the system output is a prescribed stationary covariance R(t – ~) for all t and -r greater than or equal to the ‘switch-on’ time of the system. The advantage of the ‘spectralfactorisation’ procedure described compared with those previously given is that, assuming an initial-state mean of zero, a suitable initial-state covariance is calculated as an intermediate result in the procedure. The calculation of an appropriate initial-state covariance is of interest since, if zero initial conditions are used in an attempted simulation, an undesirable time lapse may be necessary for the output covariance to be acceptable as a simulation of the prescribed stationary covariance. For the case when the system is given or is determined using alternative procedures to those described in the paper, the initial-state covariance is calculated from the solution of a linear matrix equation. The problem considered in the paper is the simulation of stationary stochastic processes with prescribed covariances using linear, finite-dimensional, time-invariant systems with white-noise input. Of particular interest is the selection of an initial-state covariance, so that the covariance of the outputs will be indistinguishable from that observed over the same time interval for the hypothetical limiting case as the initial time approaches – m. Systems which may be used in the simulation of stationary stochastic processes with prescribed covariances may be determined from any of a number of spectral-factorisation procedures, ] z,* With regard to the initial conditions, Cttrrent practice is to set these to zero and ignore the outputs for a period corresponding to a few time constants of the system. The inadequacy of this procedure has been recognised.3 In the paper two results are presented. The first is a method for selecting an initial-state covariance for a given system, so that the application of white noise at the input results in outputs that may be considered, after the switch-on, as sample functions of a stationary stochastic process; this is the best possible real-time simulation for a stationary stochastic process. All that is required in order to obtain the result is the solution of a linear matrix equation. The second result of the paper is a spectral factorisation of a specified covariance matrix using theorems from Anderson. t The procedure gives a system having a stable transferfunction matrix with a stable inverse (often required in certain optimisation problems), together with the initial-state covariance; the advantage of the particular approach presented is that all the information necessary for the simulation is given in one procedure. The key step in the procedure is the solution of a quadratic matrix equation which satisfies certain constraints. This solution, which is unique, may be found using algebraic means similar to those of Reference 4 or by determining the steadystate solution of a Riccati matrix differential equation. t The method avoids the need to carry out any of the procedures in References 1, 2 or *, which prove very complex in cases where the covariance is a matrix rather than scalar.