Violet Mwaffo

Acute caffeine administration affects zebrafish response to a robotic stimulus

Zebrafish has been recently proposed as a valid animal model to investigate the fundamental mechanisms regulating emotional behavior and evaluate the modulatory effects exerted by psychoactive compounds. In this study, we propose a novel methodological framework based on robotics and information theory to investigate the behavioral response of zebrafish exposed to acute caffeine treatment. In a binary preference test, we studied the response of caffeine-treated zebrafish to a replica of a shoal of conspecifics moving in the tank. A purely data-driven information theoretic approach was used to infer the influence of the replica on zebrafish behavior as a function of caffeine concentration. Our results demonstrate that acute caffeine administration modulates both the average speed and the interaction with the replica. Specifically, zebrafish exposed to elevated doses of caffeine show reduced locomotion and increased sensitivity to the motion of the replica. The methodology developed in this study may complement traditional experimental paradigms developed in the field of behavioral pharmacology.

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

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.

A jump persistent turning walker to model zebrafish locomotion

Zebrafish are gaining momentum as a laboratory animal species for the investigation of several functional and dysfunctional biological processes. Mathematical models of zebrafish behaviour are expected to considerably aid in the design of hypothesis-driven studies by enabling preliminary in silico tests that can be used to infer possible experimental outcomes without the use of zebrafish. This study is motivated by observations of sudden, drastic changes in zebrafish locomotion in the form of large deviations in turn rate. We demonstrate that such deviations can be captured through a stochastic mean reverting jump diffusion model, a process that is commonly used in financial engineering to describe large changes in the price of an asset. The jump process-based model is validated on trajectory data of adult subjects swimming in a shallow circular tank obtained from an overhead camera. Through statistical comparison of the empirical distribution of the turn rate against theoretical predictions, we demonstrate the feasibility of describing zebrafish as a jump persistent turning walker. The critical role of the jump term is assessed through comparison with a simplified mean reversion diffusion model, which does not allow for describing the heavy-tailed distributions observed in the fish turn rate.

Zebrafish response to a robotic replica in three dimensions

As zebrafish emerge as a species of choice for the investigation of biological processes, a number of experimental protocols are being developed to study their social behaviour. While live stimuli may elicit varying response in focal subjects owing to idiosyncrasies, tiredness and circadian rhythms, video stimuli suffer from the absence of physical input and rely only on two-dimensional projections. Robotics has been recently proposed as an alternative approach to generate physical, customizable, effective and consistent stimuli for behavioural phenotyping. Here, we contribute to this field of investigation through a novel four-degree-of-freedom robotics-based platform to manoeuvre a biologically inspired three-dimensionally printed replica. The platform enables three-dimensional motions as well as body oscillations to mimic zebrafish locomotion. In a series of experiments, we demonstrate the differential role of the visual stimuli associated with the biologically inspired replica and its three-dimensional motion. Three-dimensional tracking and information-theoretic tools are complemented to quantify the interaction between zebrafish and the robotic stimulus. Live subjects displayed a robust attraction towards the moving replica, and such attraction was lost when controlling for its visual appearance or motion. This effort is expected to aid zebrafish behavioural phenotyping, by offering a novel approach to generate physical stimuli moving in three dimensions.

Zebrafish response to 3D printed shoals of conspecifics: the effect of body size.

Recent progress in three-dimensional (3D) printing technology has enabled rapid prototyping of complex models at a limited cost. Virtually every research laboratory has access to a 3D printer, which can assist in the design and implementation of hypothesis-driven studies on animal behavior. In this study, we explore the possibility of using 3D printing technology to understand the role of body size in the social behavior of the zebrafish model organism. In a dichotomous preference test, we study the behavioral response of zebrafish to shoals of 3D printed replicas of varying size. We systematically vary the size of each replica without altering the coloration, aspect ratio, and stripe patterns, which are all selected to closely mimic zebrafish morphophysiology. The replicas are actuated through a robotic manipulator, mimicking the natural motion of live subjects. Zebrafish preference is assessed by scoring the time spent in the vicinity of the shoal of replicas, and the information theoretic construct of transfer entropy is used to further elucidate the influence of the replicas on zebrafish motion. Our results demonstrate that zebrafish adjust their behavior in response to variations in the size of the replicas. Subjects exhibit an avoidance reaction for larger replicas, and they are attracted toward and influenced by smaller replicas. The approach presented in this study, integrating 3D printing technology, robotics, and information theory, is expected to significantly aid preclinical research on zebrafish behavior.

Criteria for stochastic pinning control of networks of chaotic maps

This paper investigates the controllability of discrete-time networks of coupled chaotic maps through stochastic pinning. In this control scheme, the network dynamics are steered towards a desired trajectory through a feedback control input that is applied stochastically to the network nodes. The network controllability is studied by analyzing the local mean square stability of the error dynamics with respect to the desired trajectory. Through the analysis of the spectral properties of salient matrices, a toolbox of conditions for controllability are obtained, in terms of the dynamics of the individual maps, algebraic properties of the network, and the probability distribution of the pinning control. We demonstrate the use of these conditions in the design of a stochastic pinning control strategy for networks of Chirikov standard maps. To elucidate the applicability of the approach, we consider different network topologies and compare five different stochastic pinning strategies through extensive numerical simulations.

In-silico experiments of zebrafish behaviour: modeling swimming in three dimensions

Zebrafish is fast becoming a species of choice in biomedical research for the investigation of functional and dysfunctional processes coupled with their genetic and pharmacological modulation. As with mammals, experimentation with zebrafish constitutes a complicated ethical issue that calls for the exploration of alternative testing methods to reduce the number of subjects, refine experimental designs, and replace live animals. Inspired by the demonstrated advantages of computational studies in other life science domains, we establish an authentic data-driven modelling framework to simulate zebrafish swimming in three dimensions. The model encapsulates burst-and-coast swimming style, speed modulation, and wall interaction, laying the foundations for in-silico experiments of zebrafish behaviour. Through computational studies, we demonstrate the ability of the model to replicate common ethological observables such as speed and spatial preference, and anticipate experimental observations on the correlation between tank dimensions on zebrafish behaviour. Reaching to other experimental paradigms, our framework is expected to contribute to a reduction in animal use and suffering.

Collective Dynamics in the Vicsek and Vectorial Network Models Beyond Uniform Additive Noise

In this work, we analyze the coordination of interacting individuals in two nonlinear dynamical models that are subject to a new form of noise. Specifically, we propose extensions both to the classical Vicsek model, whereby each individual averages the orientation of its geographically proximal neighbors, and to the vectorial network model, in which the selection of neighbors is random and independent of the group geometric configuration. In the traditional forms of these models, the update rule for the individuals’ orientations is affected by additive uniform noise. Motivated by biological groups in which individuals’ turn rates exhibit sporadic and large changes, we extend the uniform additive noise model to a turn rate stochastic process. Through comprehensive numerical simulations, we demonstrate the impact of such occasional large deviations (intensity and frequency), along with the role of the neighbors’ selection process, on the coordination of the group. In addition, we establish a closed-form expression for the group polarization for the vectorial network model in the vicinity of an ordered state.

Analysis of Pairwise Interactions in a Maximum Likelihood Sense to Identify Leaders in a Group

Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader-follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of self-propelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically-treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data, but it also allows for exploring the role of psychoactive compounds on leader-follower relationships.

Measuring Zebrafish Turning Rate

Zebrafish is becoming a popular animal model in preclinical research, and zebrafish turning rate has been proposed for the analysis of activity in several domains. The turning rate is often estimated from the trajectory of the fish centroid that is output by commercial or custom-made target tracking software run on overhead videos of fish swimming. However, the accuracy of such indirect methods with respect to the turning rate associated with changes in heading during zebrafish locomotion is largely untested. Here, we compare two indirect methods for the turning rate estimation using the centroid velocity or position data, with full shape tracking for three different video sampling rates. We use tracking data from the overhead video recorded at 60, 30, and 15 frames per second of zebrafish swimming in a shallow water tank. Statistical comparisons of absolute turning rate across methods and sampling rates indicate that, while indirect methods are indistinguishable from full shape tracking, the video sampling rate significantly influences the turning rate measurement. The results of this study can aid in the selection of the video capture frame rate, an experimental design parameter in zebrafish behavioral experiments where activity is an important measure.