Information Dynamics in the Interaction between a Prey and a Predator Fish

Hu, Feng Sheng; Nie, Li-Juan; Fu, Shi‐Jian

doi:10.3390/e17107230

Cited by 31 publications

(26 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Transfer entropy has been used in fish behavioral studies to study predator-prey interactions in [30], and to elucidate the influence of a robotic stimulus on zebrafish behavior [28,29,31]. In the predator-prey study, transfer entropy is measured between a prey fish, Rhodeus ocellatus, and a predator fish, Channa argus, as they interact in an annular tank.…”

Section: Classifying Pairs Of Simulated Zebrafishmentioning

confidence: 99%

“…In this context, it may be beneficial to reduce the data dimensionality while preserving the essential dynamics. For instance, in the fish-robot interaction studies performed in [28,29,31] the dimension along the tank where the robot exhibits maximum variability in motion is utilized to compute transfer entropy, and in [30], a circular arena is divided into sixteen distinct compartments by partitioning along the angular and radial coordinates.…”

Section: Leadership In Larger Groupsmentioning

confidence: 99%

“…Model-free measures such as transfer entropy or conditional mutual information are often utilized to quantify information flow and help infer causality between the systems. These and related information theoretic measures have contributed to an improved understanding of the dynamics of complex networks [20][21][22], neuronal connectivity in brain [23,24], and causal relationships in weather forecasting [25], stock market analysis [26], social media [27], and animal behavior [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model-free information-theoretic approach to infer leadership in pairs of zebrafish

2016

View full text Add to dashboard Cite

Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.

show abstract

Section: Classifying Pairs Of Simulated Zebrafishmentioning

confidence: 99%

Section: Leadership In Larger Groupsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-free information-theoretic approach to infer leadership in pairs of zebrafish

2016

View full text Add to dashboard Cite

show abstract

“…It characterizes the interactions between components and provides important insights into the structure and dynamics of the system. This issue attracts many researchers from different fields, for instance neuroscience [7,8], physics [9,10], climatology [11,12], and zoology [13,14], etc. In econophysics, Kwon and Yang [15] analyzed the strength and direction of the information flow in 25 stock indices.…”

Section: Introductionmentioning

confidence: 99%

Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Wang

Xia

2018

Entropy

View full text Add to dashboard Cite

This paper applies effective transfer entropy to research the information transfer in the Chinese stock market around its crash in 2015. According to the market states, the entire period is divided into four sub-phases: the tranquil, bull, crash, and post-crash periods. Kernel density estimation is used to calculate the effective transfer entropy. Then, the information transfer network is constructed. Nodes’ centralities and the directed maximum spanning trees of the networks are analyzed. The results show that, in the tranquil period, the information transfer is weak in the market. In the bull period, the strength and scope of the information transfer increases. The utility sector outputs a great deal of information and is the hub node for the information flow. In the crash period, the information transfer grows further. The market efficiency in this period is worse than that in the other three sub-periods. The information technology sector is the biggest information source, while the consumer staples sector receives the most information. The interactions of the sectors become more direct. In the post-crash period, information transfer declines but is still stronger than the tranquil time. The financial sector receives the largest amount of information and is the pivot node.

show abstract

“…The number of applications of concepts and techniques from information theory to analysis of animal movement in the literature is limited. However, information-theoretic metrics have been used in the past for example to study information flow in animal-robot interactions [13] as well as predator-prey relationships [45] [29] in animals.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Animal Movement Patterns using Information Theory: a Primer

Owoeye

Musolesi

Hailes

2018

Preprint

View full text Add to dashboard Cite

Understanding the movement patterns of animals across different spatio-temporal scales, conditions, habitats and contexts is becoming increasingly important for addressing a series of questions in animal behaviour studies, such as mapping migration routes, evaluating resource use, modelling epidemic spreading in a population, developing strategies for animal conservation as well as understanding several emerging patterns related to feeding, growth and reproduction. In recent times, information theory has been successfully applied in several fields of science, in particular for understanding the dynamics of complex systems and characterizing adaptive social systems, such as dynamics of entities as individuals and as part of groups.In this paper, we describe a series of non-parametric information-theoretic measures that can be used to derive new insights about animal behaviour with a specific focus on movement patterns namely Shannon entropy, Mutual information, Kullback-Leibler divergence and Kolmogorov complexity. In particular, we believe that the metrics presented in this paper can be used to formulate new hypotheses that can be verified potentially through a set of different observations. We show how these measures can be used to characterize the movement patterns of several animals across different habitats and scales. Specifically, we show the effectiveness in using Shannon entropy to characterize the movement of sheep with Batten disease, mutual information to measure association in pigeons, Kullback Leibler divergence to study the flights of Turkey vulture, and Kolmogorov complexity to find similarities in the movement patterns of animals across different scales and habitats. Finally, we discuss the limitations of these methods and we outline the challenges in this research area.Information theory has always played an important role in biology [24] [41]. It is a field that is devoted to studying the storage, communication and quantification of information founded by Claude E. Shannon in his influential paper [55] and lies at the interface of mathematics, statistics, computer science and electrical engineering. While initial research in this field was mainly theoretical, we have witnessed a plethora of practical applications in the past decades. For example, concepts and techniques from this field have been used in several fields such as neurobiology [51], pattern recognition [16], cryptology [61], bioinformatics [46], quantum computing [39] and complex systems [37] [42] with significant success.Recently, due to technological advances, low cost miniaturized sensors have been increasingly adopted for tracking the behaviour of animals across different scales and 1/21 2/21

show abstract

Information Dynamics in the Interaction between a Prey and a Predator Fish

Cited by 31 publications

References 37 publications

Model-free information-theoretic approach to infer leadership in pairs of zebrafish

Model-free information-theoretic approach to infer leadership in pairs of zebrafish

Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Characterization of Animal Movement Patterns using Information Theory: a Primer

Contact Info

Product

Resources

About