Pink-noise behaviour of biosystems

Szendró, Péter; Vincze, Gyula; Szász, András

doi:10.1007/s002490100143

Cited by 107 publications

(61 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The slope S of the power spectrum was very similar across different pinned (red bars) and free (blue bars) leeches, as shown by the slope distributions reported in Figure 4C. The value of the slope S is highly indicative of the kind of process analyzed and can be considered, in this case, as a measure of the correlation of the different movements composing the behavior (Berg, 1993;Szendro et al, 2001). The observed similarity suggests that, although very different and very irregular, all exploratory episodes belong to the same dynamics.…”

Section: Analysis Of Exploratory Statesmentioning

confidence: 78%

Statistics of Decision Making in the Leech

García‐Pérez¹,

Mazzoni²,

Zoccolan³

et al. 2005

J. Neurosci.

View full text Add to dashboard Cite

Animals continuously decide among different behaviors, but, even in invertebrates, the mechanisms underlying choice and decision are unknown. In this article, leech spontaneous behavior was tracked and quantified for up to 12 h. We obtained a statistical characterization, in space and time domains, of the decision processes underlying selection of behavior in the leech. We found that the spatial distribution of leech position in a uniform environment is isotropic (the same in all directions), but this isotropy is broken in the presence of localized external stimuli. In the time domain, transitions among behaviors can be described by a Markov process, the structure of which (allowed states and transitions) is highly conserved across individuals. Finally, a wide range of recurrent, deterministic motifs was identified in the apparently irregular and unstructured exploratory behavior. These results provide a rigorous description of the inner dynamics that control the spontaneous and continuous flow of behavioral decisions in the leech.

show abstract

Section: Analysis Of Exploratory Statesmentioning

confidence: 78%

Statistics of Decision Making in the Leech

García‐Pérez¹,

Mazzoni²,

Zoccolan³

et al. 2005

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…We only consider the main chain atoms (N, CA, C and O) because these atoms are bound to each other in the protein backbone. Using B-factors values of the main chain atoms for the lysozymes complexes we have obtained spatial series and every series of data was subjected to the following investigation methods: the spectral analysis [24], the detrended fluctuation analysis [25] and the Hurst exponent calculation [26]. We make only a short description of these techniques here, a detailed description could be found by reading the literature quoted above.…”

Section: Methodsmentioning

confidence: 99%

“…In order to remove this non-stationary component, the DFA method is used. It allows to compute the scaling coefficient, α, which is related to the spectral exponent by [24]: β th = 2α − 1.…”

Section: Methodsmentioning

confidence: 99%

Long-Range Correlation in Atomic Vibration οf Chicken Lysozyme Backbone

2009

View full text Add to dashboard Cite

Within this study we use methods such as spectral analysis that gives the spectral coefficient β, detrended fluctuations analysis that gives the scaling exponent α and the determination of Hurst exponent (H) to analyze the spatial series corresponding to the temperature factors of N, C-alpha, C and O atoms of 14 complexes of the chicken lysosyme. The mean values of the investigated parameters obtained for the 14 complexes are: β = 1.779 ± 0.086, α = 1.382 ± 0.009 and H = 0.916. These values reveal long-range correlation in atomic vibrations corresponding to the chicken lysozyme backbone.

show abstract

“…As it is shown [20] [21], it can discover the abnormalities very early, and/or it is able to check the ageing-status of the human body [22]. Earlier, we had investigated the method theoretically [23] [24].…”

Section: Connection Between the Fluctuation And The Induced Noise Theorymentioning

confidence: 99%

Intrinsic Noise Monitoring of Complex Systems

Szász¹,

Szigeti²,

Szász³

2017

OJBIPHY

View full text Add to dashboard Cite

The power-density function of the noise spectrum of open and complex systems changes by the power of frequency. We show that the fluctuation origin and the noise-powered description are equivalent to describe the colored noise power density. Based on this, we introduce a scale-independent invariant for monitoring the dynamics of the complex system. The monitoring of the noise spectrum of the system specifies the forecast of failure, the timing of desired regular corrections and/or the assessed operation life of the system, indicating the possible faults before it happens, predicting deterioration like wear/tear, fatigue in the still properly working system. These considerations are highly applicable to living systems and their preventive care.

show abstract

Pink-noise behaviour of biosystems

Cited by 107 publications

References 0 publications

Statistics of Decision Making in the Leech

Statistics of Decision Making in the Leech

Long-Range Correlation in Atomic Vibration οf Chicken Lysozyme Backbone

Intrinsic Noise Monitoring of Complex Systems

Contact Info

Product

Resources

About