Scaling and Intermittency in Animal Behavior

Scale-invariant spatial or temporal patterns and L\'evy flight motion have been observed in a large variety of biological systems. It has been argued that animals in general might perform L\'evy flight motion with power law distribution of times between two changes of the direction of motion. Here we study the temporal behaviour of nesting gilts. The time spent by a gilt in a given form of activity has power law probability distribution without finite average. Further analysis reveals intermittent eruption of certain periodic behavioural sequences which are responsible for the scaling behaviour and indicates the existence of a critical state. We show that this behaviour is in close analogy with temporal sequences of velocity found in turbulent flows, where random and regular sequences alternate and form an intermittent sequence.Comment: 10 page

Neural Correlates of Social Behavior in Mushroom Body Extrinsic Neurons of the Honeybee Apis mellifera

The social behavior of honeybees (Apis mellifera) has been extensively investigated, but little is known about its neuronal correlates. We developed a method that allowed us to record extracellularly from mushroom body extrinsic neurons (MB ENs) in a freely moving bee within a small but functioning mini colony of approximately 1,000 bees. This study aimed to correlate the neuronal activity of multimodal high-order MB ENs with social behavior in a close to natural setting. The behavior of all bees in the colony was video recorded. The behavior of the recorded animal was compared with other hive mates and no significant differences were found. Changes in the spike rate appeared before, during or after social interactions. The time window of the strongest effect on spike rate changes ranged from 1 s to 2 s before and after the interaction, depending on the individual animal and recorded neuron. The highest spike rates occurred when the experimental animal was situated close to a hive mate. The variance of the spike rates was analyzed as a proxy for high order multi-unit processing. Comparing randomly selected time windows with those in which the recorded animal performed social interactions showed a significantly increased spike rate variance during social interactions. The experimental set-up employed for this study offers a powerful opportunity to correlate neuronal activity with intrinsically motivated behavior of socially interacting animals. We conclude that the recorded MB ENs are potentially involved in initiating and controlling social interactions in honeybees

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Probabilistic models of individual and collective animal behavior

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.Comment: 26 pages, 11 figure

Phoresy by Pseudoscorpions

Excerpt: Phoretic behavior involving a non-parasitic association of a larger animal by a smaller animal resulting in transportation is well-documented in some pseudoscorpions. Muchmore (1971) summarized the records of pseudoscorpion phoresy in Central and North America as presented by Beier and as published since 1948

Embedded neural network for real-time animal behavior classification

Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large amounts of obtained data have to be processed off-line in order to classify different behaviors.There are recent research projects focused on designing monitoring systems capable of measuring someanimals’ parameters in order to recognize and monitor their gaits or behaviors. However, network unre- liability and high power consumption have limited their applicability.In this work, we present an animal behavior recognition, classification and monitoring system based ona wireless sensor network and a smart collar device, provided with inertial sensors and an embeddedmulti-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviorsbased on the collected information. In similar works, classification mechanisms are implemented in aserver (or base station). The main novelty of this work is the full implementation of a reconfigurableneural network embedded into the animal’s collar, which allows a real-time behavior classification andenables its local storage in SD memory. Moreover, this approach reduces the amount of data transmittedto the base station (and its periodicity), achieving a significantly improving battery life. The system hasbeen simulated and tested in a real scenario for three different horse gaits, using different heuristics andsensors to improve the accuracy of behavior recognition, achieving a maximum of 81%.Junta de Andalucía P12-TIC-130

The development of laboratory animal science and animal care of legislation and the consummation

Laboratory animal science is the use of non-human animals in experiments to obtain new knowledge and new technologies in biomedical research and testing. In order to develop science and technology, the human carried out a large number of animal experiments, these experiments greatly expanded the vision of related research field, and make a great contribution to human beings. Meanwhile, animal experiments also bring us a certain extent of negative effects. Countries around the world have adopted legislative measures to regulate behavior of animal experiments, but in the process of legislation and enforcement are not wholly satisfactory. On the basis of present situation of laboratory animal science and existing problems, with the comparison of animal welfare act between Europe and China, the author puts forward the ideas of perfecting experimental animals’ laws and its enforcement proposals