skip to main content

Title: A lexical approach for identifying behavioural action sequences
Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called “BASS” to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in more » the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data. « less
; ; ; ; ;
Faisal, Aldo A
Award ID(s):
Publication Date:
Journal Name:
PLOS Computational Biology
Page Range or eLocation-ID:
Sponsoring Org:
National Science Foundation
More Like this
  1. Locomotion is an essential behaviour for the survival of all animals. The neural circuitry underlying locomotion is therefore highly robust to a wide variety of perturbations, including injury and abrupt changes in the environment. In the short term, fault tolerance in neural networks allows locomotion to persist immediately after mild to moderate injury. In the longer term, in many invertebrates and vertebrates, neural reorganization including anatomical regeneration can restore locomotion after severe perturbations that initially caused paralysis. Despite decades of research, very little is known about the mechanisms underlying locomotor resilience at the level of the underlying neural circuits and coordination of central pattern generators (CPGs). Undulatory locomotion is an ideal behaviour for exploring principles of circuit organization, neural control and resilience of locomotion, offering a number of unique advantages including experimental accessibility and modelling tractability. In comparing three well-characterized undulatory swimmers, lampreys, larval zebrafish and Caenorhabditis elegans, we find similarities in the manifestation of locomotor resilience. To advance our understanding, we propose a comparative approach, integrating experimental and modelling studies, that will allow the field to begin identifying shared and distinct solutions for overcoming perturbations to persist in orchestrating this essential behaviour.
  2. Abstract

    GABAA receptors mediate rapid responses to the neurotransmitter gamma-aminobutyric acid and are robust regulators of the brain and spinal cord neural networks that control locomotor behaviors, such as walking and swimming. In developing zebrafish, gross pharmacological blockade of these receptors causes hyperactive swimming, which is also a feature of many zebrafish epilepsy models. Although GABAA receptors are important to control locomotor behavior, the large number of subunits and homeostatic compensatory mechanisms have challenged efforts to determine subunit-selective roles. To address this issue, we mutated each of the 8 zebrafish GABAA α subunit genes individually and in pairs using a CRISPR-Cas9 somatic inactivation approach and, then, we examined the swimming behavior of the mutants at 2 developmental stages, 48 and 96 h postfertilization. We found that disrupting the expression of specific pairs of subunits resulted in different abnormalities in swimming behavior at 48 h postfertilization. Mutation of α4 and α5 selectively resulted in longer duration swimming episodes, mutations in α3 and α4 selectively caused excess, large-amplitude body flexions (C-bends), and mutation of α3 and α5 resulted in increases in both of these measures of hyperactivity. At 96 h postfertilization, hyperactive phenotypes were nearly absent, suggesting that homeostatic compensation was ablemore »to overcome the disruption of even multiple subunits. Taken together, our results identify subunit-selective roles for GABAA α3, α4, and α5 in regulating locomotion. Given that these subunits exhibit spatially restricted expression patterns, these results provide a foundation to identify neurons and GABAergic networks that control discrete aspects of locomotor behavior.

    « less
  3. ABSTRACT Whereas many fishes swim steadily, zebrafish regularly exhibit unsteady burst-and-coast swimming, which is characterized by repeated sequences of turns followed by gliding periods. Such a behavior offers the opportunity to investigate the hypothesis that negative mechanical work occurs in posterior regions of the body during early phases of the turn near the time of maximal body curvature. Here, we used a modified particle image velocimetry (PIV) technique to obtain high-resolution flow fields around the zebrafish body during turns. Using detailed swimming kinematics coupled with body surface pressure computations, we estimated fluid–structure interaction forces and the pattern of forces and torques along the body during turning. We then calculated the mechanical work done by each body segment. We used estimated patterns of positive and negative work along the body to evaluate the hypothesis (based on fish midline kinematics) that the posterior body region would experience predominantly negative work. Between 10% and 20% of the total mechanical work was done by the fluid on the body (negative work), and negative work was concentrated in the anterior and middle areas of the body, not along the caudal region. Energetic costs of turning were calculated by considering the sum of positive and negativemore »work and were compared with previous metabolic estimates of turning energetics in fishes. The analytical workflow presented here provides a rigorous way to quantify hydrodynamic mechanisms of fish locomotion and facilitates the understanding of how body kinematics generate locomotor forces in freely swimming fishes.« less
  4. Abstract Background Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors. Methods We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data. Results HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate asmore »those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale. Conclusions The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.« less
  5. Social status-dependent modulation of neural circuits has been investigated extensively in vertebrate and invertebrate systems. However, the effects of social status on neuromodulatory systems that drive motor activity are poorly understood. Zebrafish form a stable social relationship that consists of socially dominant and subordinate animals. The locomotor behavior patterns differ according to their social ranks. The sensitivity of the Mauthner startle escape response in subordinates increases compared to dominants while dominants increase their swimming frequency compared to subordinates. Here, we investigated the role of the endocannabinoid system (ECS) in mediating these differences in motor activities. We show that brain gene expression of key ECS protein pathways are socially regulated. Diacylglycerol lipase (DAGL) expression significantly increased in dominants and significantly decreased in subordinates relative to controls. Moreover, brain gene expression of the cannabinoid 1 receptor (CB 1 R) was significantly increased in subordinates relative to controls. Secondly, increasing ECS activity with JZL184 reversed swimming activity patterns in dominant and subordinate animals. JZL184 did not affect the sensitivity of the startle escape response in dominants while it was significantly reduced in subordinates. Thirdly, blockage of CB 1 R function with AM-251 had no effect on dominants startle escape response sensitivity, but startlemore »sensitivity was significantly reduced in subordinates. Additionally, AM-251 did not affect swimming activities in either social phenotypes. Fourthly, we demonstrate that the effects of ECS modulation of the startle escape circuit is mediated via the dopaminergic system specifically via the dopamine D1 receptor. Finally, our empirical results complemented with neurocomputational modeling suggest that social status influences the ECS to regulate the balance in synaptic strength between excitatory and inhibitory inputs to control the excitability of motor behaviors. Collectively, this study provides new insights of how social factors impact nervous system function to reconfigure the synergistic interactions of neuromodulatory pathways to optimize motor output.« less