Unraveling the fluctuations of animal motor activity

Abstract

Human and animal behavior exhibits power law correlations whose origin is controversial. In this work, the spontaneous motion of laboratory rodents was recorded during several days. It is found that animal motion is scale-free and that the scaling is introduced by the inactivity pauses both by its length as well as by its specific ordering. Furthermore, the scaling is also demonstrable in the rates of event's occurrence. A comparison with related results in humans is made and candidate models are discussed to provide clues for the origin of such dynamics. © 2009 American Institute of Physics. ͓DOI: 10.1063/1.3211189͔ Recent findings of heavy tailed distributions in the patterns of human activity reemphasized our poor understanding on the mechanisms responsible for such type of dynamics. To shed light on the most significant features of these fluctuations, the problem is here oversimplified by studying a much elementary system: the spontaneous motion of rodents recorded during several days. The analysis of the animal motion reveals a robust scaling not only in univariate distributions, comparable with the results previously reported in humans, but also in its correlation structure. It is shown that the most relevant features of the experimental results can be replicated by the statistics of the activation-threshold model proposed in another context by Davidsen and Schuster. It constitutes an alternative mechanism to queuing, cascading, and nonhomogeneous processes, currently contemplated as candidates to account for the statistics of diverse human activities, but that seem not suitable for motion patterns.

Assessment of long-range correlation in animal behavior time series: The temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)

“…Also, the mean value of S M remains fairly constant throughout the day, ranging between -0.79 and -1.03 (Figure 5h). The majority of the studies that evaluate the distribution of the duration of behavioral events combine the data of many animals to obtain the frequency histograms, especially if the test has a short duration [13,26,35,36]. In this study when the data from the one-hour records are combined, similar values were obtained of S I = −1.…”

“…In addition, the value of the scaling exponent for immobility events (S I ) were lower in quail and in mosquito larva (-1.48 and -1.15, respectively) compared to the values observed by Anteneodo and Chialvo [35]. In both species the scaling exponent of mobility events S M (-2.48 in quail and -2.44 in mosquito larvae) was larger than S I indicating that mobility events are frequently of shorter duration than immobility events.…”

“…Anteneodo and Chialvo [35] showed in rat, evaluated in their home-cage during 9 consecutive days, that from few seconds to several thousand seconds (about 1 hour), the distribution of inter-event times (equivalent to immobility) decays as a power law (with scaling exponent falling within the interval of 1.75 ± 0.05) for all six animals. In contrast, the distribution of the duration of motion (mobility) episodes did not possess a characteristic time scale, and was described by a superposition of two exponentials with characteristic times of the order of 1 and 4 s, close to the smaller data resolution and to the average duration of motion episodes, respectively.…”

Simulated shift work in rats perturbs multiscale regulation of locomotor activity

“…Outputs from a wide variety of physiological systems, such as heart rate and motor activity, exhibit complex fluctuation patterns characterized by fractal/ scale-invariant structures with properties that remain invariant over a wide range of timescales [1][2][3][4][5][6][7][8][9]. These fractal patterns appear to be a hallmark of healthy physiology because they are robust in healthy physiological systems but are significantly disrupted or abolished in degraded systems that are more vulnerable to catastrophic events and less adaptable to perturbations [2,4,10 -13].…”

Criticality at Work: How Do Critical Networks Respond to Stimuli?

“…For instance, sleep-wake transitions have been shown to be governed by power law distributions across mammalian species, suggesting scale-invariant features in the mechanisms of sleep control [48]. Nontrivial scaling properties have also been found in the fluctuations of the motor activity of rats, with power laws governing the distribution of time intervals between consecutive movements [49].…”