Stochastic Resonance of Ensemble Neurons for Transient Spike Trains: A Wavelet Analysis

By using the wavelet transformation (WT), we have analyzed the response of an ensemble of $N$ (=1, 10, 100 and 500) Hodgkin-Huxley (HH) neurons to {\it transient} $M$-pulse spike trains ($M=1-3$) with independent Gaussian noises. The cross-correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the {\it denoising} method within the WT, by which the noise contribution is extracted from output signals. Although the response of a single (N=1) neuron to sub-threshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross-correlation and SNR is shown to be much improved by increasing the value of $N$: a population of neurons play an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for supra-threshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for sub-threshold inputs.Comment: 20 pages, 4 figure

Novel approach to analysing large data sets of personal sun exposure measurements

Personal sun exposure measurements provide important information to guide the development of sun awareness and disease prevention campaigns. We assess the scaling properties of personal ultraviolet radiation (pUVR) sun exposure measurements using the wavelet transform (WT) spectral analysis to process long-range, high-frequency personal recordings collected by electronic UVR dosimeters designed to measure erythemal UVR exposure. We analysed the sun exposure recordings of school children, farmers, marathon runners and outdoor workers in South Africa, and construction workers and work site supervisors in New Zealand. We found scaling behaviour in all the analysed pUVR data sets. We found that the observed scaling changes from uncorrelated to long-range correlated with increasing duration of sun exposure. Peaks in the WT spectra that we found suggest the existence of characteristic times in sun exposure behaviour that were to some extent universal across our data set. Our study also showed that WT measures enable group classification, as well as distinction between individual UVR exposures, otherwise unattainable by conventional statistical methods

Beneficial randomness of signals in a neuronal circuit

We have analyzed spontaneous discharge dynamics of fasimotor neurons, by applying the so-called detrended fluctuation analysis, which is a modification of the random walk model analysis. Besides, we applied the wavelet analysis method to the same problem. By using these methods we have found evidence for the white noise characteristics of the time series generated by the neuron discharges. We concluded that such a discharge. dynamics represents the requisite noisy component for occurrence of the stochastic resonance mechanism in the neural coordination. of muscle spindles. This finding appears to be very intriguing; since it provided for the first time statistical characterization of the neuronal noise

Wavelet analysis of discharge dynamics of fusimotor neurons

We study the interspike intervals (ISI) time series of the spontaneous fusimotor neuron activity by applying the wavelet transform analysis and confirm the existence of the white noise characteristics of the ISI time series. This means that the neuron activity may serve as the requisite noisy component for occurrence of the stochastic resonance mechanism in the neural coordination of muscle spindles. Besides, we apply the multifractal formalism adapted for the wavelet transform time series analysis. Thus, we have established the multifractality of the ISI data and achieved an additional insight into fusimotor discharge dynamics

Detecting long-range correlations in time series of neuronal discharges

We have studied the discharge dynamics of dorsal horn neurons (DHN) by applying the detrended fluctuation analysis (DFA) and the wavelet transform (WT) technique. We have adopted that discharge dynamics is manifested by the random time series of the interspike intervals (ISI), that is, by intervals between two consecutive neuronal electrical activities. In all cases studied, we found two different power-law type behaviors across ISI enumeration scale, that are separated by a crossover region. Our results reveal that complex neuronal dynamics may change in the presence of external stimulation, which is manifested by changing the noise characteristics that appear before the crossover region (the noise after the crossover region is of the 1/f type)

Detecting long-range correlations in time series of dorsal horn neuron discharges

We have studied the discharge dynamics of dorsal horn neurons by applying the detrended fluctuation analysis and the wavelet transform technique. We have adopted that the neuronal discharge dynamics is manifested by the random time series of interspike intervals. In all cases studied, we found two different power-law type behaviors across interspike intervals enumeration scale, that are separated by crossover regions (which implies existence of two different types of neuronal noise). Our results reveal that complex neuronal dynamics may change in the presence of external stimulation, manifested by changing of the noise characteristics that appear before crossover

Detrended fluctuation analysis of time series of a firing fusimotor neuron

We study the interspike intervals (ISI) time series of the spontaneous fusimotor neuron activity by applying the detrended fluctuation analysis that is a modification of the random walk model analysis. Thus, we have found evidence for the white noise characteristics of the ISI time series, which means that the fusimotor activity does not possess temporal correlations. We conclude that such an activity represents the requisite noisy component for occurrence of the stochastic resonance mechanism in the neural coordination of muscle spindles

Wavelet spectral analysis of teleconnection indices and activities of beryllium-7 and lead-210 in ground level air in Belgrade, Serbia

Activities of beryllium-7 and lead-210 are monitored in ground level air in Belgrade, Serbia. The measuring sites are located at the Institute of Nuclear Sciences Vinča. The activities are determined on HPGe detectors by standard gamma spectrometry. Five teleconnection indices of large scale atmospheric circulation: North Atlantic Oscillation, East Atlantic Pattern, East Atlantic/West Russia Pattern, Scandinavia Pattern, and Polar/Eurasia Pattern are obtained from the data archive of the United States National Oceanic and Atmospheric Administration's Climate Prediction Center. The collected time series consist of monthly values and span more than two decades: beryllium-7 since 1991, lead-210 since 1985, and the teleconnection indices since 1950, thus offering data arrays of sufficient lengths for wavelet spectral analysis. A relation between the radionuclides' activities and the indices is first investigated using Pearson's correlation coefficients. The computed coefficients do not indicate a linear relationship between the variables. However, the wavelet spectral analysis shows a number of common characteristic frequencies in the data arrays. The annual cycle of all the variables is clearly evident. A common time period of two to three years is also found, as well as a higher frequency variability corresponding to five to six months

Wavelet spectral analysis of teleconnection indices and activities of beryllium-7 and lead-210 in ground level air in Belgrade, Serbia

Activities of beryllium-7 and lead-210 are monitored in ground level air in Belgrade, Serbia. The measuring sites are located at the Institute of Nuclear Sciences Vinča. The activities are determined on HPGe detectors by standard gamma spectrometry. Five teleconnection indices of large scale atmospheric circulation: North Atlantic Oscillation, East Atlantic Pattern, East Atlantic/West Russia Pattern, Scandinavia Pattern, and Polar/Eurasia Pattern are obtained from the data archive of the United States National Oceanic and Atmospheric Administration's Climate Prediction Center. The collected time series consist of monthly values and span more than two decades: beryllium-7 since 1991, lead-210 since 1985, and the teleconnection indices since 1950, thus offering data arrays of sufficient lengths for wavelet spectral analysis. A relation between the radionuclides' activities and the indices is first investigated using Pearson's correlation coefficients. The computed coefficients do not indicate a linear relationship between the variables. However, the wavelet spectral analysis shows a number of common characteristic frequencies in the data arrays. The annual cycle of all the variables is clearly evident. A common time period of two to three years is also found, as well as a higher frequency variability corresponding to five to six months