Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Animals exhibit diverse behavioral responses, such as exploration and avoidance, to novel cues in the environment. However, it remains unclear how dopamine neuron-related novelty responses influence behavior. Here, we characterized dynamics of novelty exploration using multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq). Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability were explained by a novel reinforcement learning (RL) model of threat prediction, in which behavior arises from a novelty-induced initial threat prediction (akin to shaping bonus), and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.

Coming and Going in Loops: Participatory Modelling of a System with All its Complexity

Social marketing is currently involved in pursuing several important theoretical and methodological goals pertaining to wide-scale behavior change. The lack of complex system understanding via highly participatory processes and feedback loops is a major impediment for systemic behavior change. The purpose of this paper is to show how the implementation of participatory modelling to explore networks of feedback loops can empower social marketing in capturing system complexity. As a case study, a group of system stakeholders qualitatively modelled a cycling system in a city setting to uncover the system's core behavioral dynamics. This participatory modelling process revealed that the interactions within and between three feedback loops were mainly responsible for the cycling system issues. These feedback loops were: (a) output-based and autocratic decision-making, (b) an abundance of conflicted interests and (c) the reinforcement of a car-dominant paradigm in people's minds. The paper contributes to understanding the potential of participatory modelling for multi-level behavior change.

Comprehensive analysis of behavioral dynamics in the protochordate Ciona intestinalis

Locomotion is broadly conserved in the animal kingdom, yet our understanding of how complex locomotor behaviors are generated and have evolved is relatively limited by the lack of an accurate description of their structural organization. Here we take a neuroethological approach to break down the motor behavioral repertoire of one of our nearest invertebrate relative, the protochordate Ciona intestinalis, into basic building blocks. Using machine vision, we track thousands of swimming larvae to obtain a feature-rich description of larval swimming and show that most of the postural variance can be captured by six basic shapes, which we term Eigencionas. Using multiple complementary approaches, we built representations of the larval behavioral dynamics and systematically reveal the global structure of behavior. By employing matrix profiling and subsequence time-series clustering, we reveal that Ciona swimming is rich in stereotyped behavioral motifs. Combining pharmacological inhibition of bioamine signaling with Hidden Markov Model we discover underlying behavioral states including multiple modes of roaming and dwelling. Finally, performing a spatio-temporal embedding of the postural features onto a behavioral space provides insight into the behavioral repertoire by project it to a low-dimensional space and highlights subtle light stimulus evoked behavioral differences. Taken together, Ciona larvae generate their spontaneous swimming and visuomotor behavioral repertoire by altering both their motor modules and transitions between, which are amenable to pharmacological perturbations, facilitating future functional and mechanistic investigations.

Recurrence eigenvalues of movements from brain signals

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

A Standardized Protocol for Quantifying the Behavioral Dynamics of Food-seeking in Mice

Food is generally hidden in a natural environment and require free-living animals to search for it. Although such food-seeking behaviors involve motivation and exploration, previous studies examined food-seeking simply by measuring the time spent in the food zone or the frequency of pursuing food-cued context. Moreover, after discovering food, animals need to taste and smell it in order to evaluate their nutritional value or possible toxicity. However, researchers could not easily distinguish food-seeking from food-evaluating behaviors because food was visible or accessible throughout each test. Herein, we describe a behavioral protocol that triggers animals to show the behavioral dynamics of food-seeking (e.g., navigation, nose-digging, and paw-digging) and that exclusively elicits food-seeking without provoking any other food-evaluating behaviors. First, we prepared an open-field box with the floor covered with bedding. After we hid foods under the bedding of each corner, the test mice were habituated in this arena for four days (pre-test phase). On the next day (test phase), they were placed under the same conditions, but the foods previously hidden were removed. This process enabled the mice to perceive their surroundings as a food-hidden environment, which induced the animal to exhibit sustained food-seeking. In conclusion, the protocol presented here is a powerful method for provoking multiple forms of food-seeking and quantifies food-seeking independently from other food-related behavioral stages.

Behavioral dynamics of pedestrian’s movements: Theoretical aspects of commercial arterial road frontage activities

The common scientific approaches to the reasoning of problems are mathematical reasoning or statistical reasoning. Mathematical or formal reasoning is usually deductive, therein one reason from general assumptions to specifics using symbolic logic and axioms for multi criteria decision-making. Mathematical probability, which is the basis of all statistical theories, had its beginning in the past. The aim of this paper is to explore a number of the mathematical and statistical aspects of the disposition and behavior of road frontage activities, which are of importance in pedestrian behavior as considered. It's shown that number of crossings from right to left is proportional to the pedestrian on the right (PXRL ∝ NR) and therefore, the number of crossings left to right is proportional to the pedestrians on the left (PXLR ∝ NL). Frequency distributions of the pedestrians generated for a given shopping string arterial were of 4 kinds, one related to pedestrians passing through not crossing the road, not going into and out of outlets. The second kind related to pedestrians crossing the road for the aim of going into and out of outlets, etc. the third kind related to pedestrian going into shops and eventually, the fourth kind related to others, e.g. Pedestrians generated from parking vehicles, buses, etc. A formula is given for the frequency with crossing from left to right and right to leave based on the land-use activities on the left and right. In considering the capacity of road systems it should be remembered that increases in traffic flow generally produce corresponding decreases in speed. However, it's an assumption that a rise in population generated along the footpath can cause the crossing the road, and usually produce corresponding decreases in vehicle speed. The paper concludes with a constatation of the pedestrian movements at a continuing rate that expressed in mathematical form.

Human-Robot Interaction in Groups: Methodological and Research Practices

Understanding the behavioral dynamics that underline human–robot interactions in groups remains one of the core challenges in social robotics research. However, despite a growing interest in this topic, there is still a lack of established and validated measures that allow researchers to analyze human–robot interactions in group scenarios; and very few that have been developed and tested specifically for research conducted in-the-wild. This is a problem because it hinders the development of general models of human–robot interaction, and makes the comprehension of the inner workings of the relational dynamics between humans and robots, in group contexts, significantly more difficult. In this paper, we aim to provide a reflection on the current state of research on human–robot interaction in small groups, as well as to outline directions for future research with an emphasis on methodological and transversal issues.

Sex-dependent parameters of social behavior show marked variations between distinct laboratory mouse strains and their mixed offspring

Background: The survival of individuals of gregarious species depends on their ability to properly form social interactions. In humans, atypical social behavior is a hallmark of several psychopathological conditions, such as depression and autism spectrum disorder, many of which have sex-specific manifestations. Various strains of laboratory mice are used to reveal the mechanisms mediating typical and atypical social behavior in mammals. Methods: Here we used three social discrimination tests (social preference, social novelty preference, and sex preference) to characterize social behavior in males and females of three widely used laboratory mouse strains (C57BL/6J, BALB/c, and ICR). Results: We found marked sex- and strain-specific differences in the preference exhibited by subjects in a test-dependent manner. Interestingly, we found some characteristics that were strain-dependent, while others were sex-dependent. Moreover, even in the social preference test, where both sexes of all strains prefer social over object stimuli, we revealed sex- and strain-specific differences in the behavioral dynamics. We then cross-bred C57BL/6J and BALB/c mice and demonstrated that the offspring of such cross-breeding exhibit a profile of social behavior which is different from both parental strains and depends on the specific combination of parental strains. Conclusions: We conclude that social behavior of laboratory mice is highly sex- and strain-specific and strongly depends on genetic factors.

Networks of Causal Linkage Between Eigenmodes Characterize Behavioral Dynamics of Caenorhabditis elegans

Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans, where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called “eigenworms”). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create “interaction profiles” to represent an individual’s behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons.

Emergent intra-pair sex differences and behavioral coordination in pair bonded prairie voles

In pair bonding animals, coordinated behavior between partners is required for the pair to accomplish shared goals such as raising young. Despite this, experimental designs rarely assess the behavior of both partners within a bonded pair. Thus, we lack an understanding of the interdependent behavioral dynamics between partners that likely facilitate relationship success. To identify intra-pair behavioral correlates of pair bonding, we used socially monogamous prairie voles, a species in which females and males exhibit both overlapping and distinct pair bond behaviors. We tested both partners using social choice and non-choice tests at short- and long-term pairing timepoints. Females developed a preference for their partner more rapidly than males, with preference driven by different behaviors in each sex. Further, as bonds matured, intra-pair behavioral sex differences and coordinated behavior emerged – females consistently huddled more with their partner than males did, and partner huddle time became correlated between partners. When animals were allowed to freely interact with a partner or a novel in sequential free interaction tests, pairs spent more time interacting together than either animal did with a novel. Pair interaction was correlated with female, but not male, behavior. Via a social operant paradigm, we found that pair-bonded females, but not males, are more motivated to access and huddle with their partner than a novel vole. Together, our data indicate that as pair bonds mature, sex differences and coordinated behavior emerge, and that these intra-pair behavioral changes are likely organized and driven by the female animal.