The adverse effects of chronic social stress on learning and the role of serotonin quantified by a binary logistic regression model in individual crickets (Gryllus bimaculatus)

The ability to learn and change future behaviour based on past experiences is crucial for the life and survival of animals. For various behaviours exhibited by animals it is clear that in a seemingly homogeneous population not all individuals behave the same way, even in invertebrates. In crickets (Gryllus bimaculatus), a model system for the mechanisms of intra-specific aggression, agonistic experiences with the underlying impact of neuromodulators have been identified as a cause of inter-individual differences. For mammals and humans, the experience of adversity and stress can have detrimental effects on cognitive abilities and chronic defeat stress is used as a model for depression. In crickets the equivalent, the chronic social defeat stress paradigm, has been established. This thesis first sets out to construct a new model for measuring a conditioned response from multiple behavioural aspects and quantify learning in individual crickets. Video tracking of responses revealed behavioural variables that were included in a binary logistic regression analysis, whereas the resulting multi-variable model proves to be superior to other models constructed and can give the probability of an individual exhibiting a conditioned response. With this, learning indices can be calculated for each individual trained in a differential appetitive olfactory paradigm. With the method at hand, this thesis reveals that the experience of chronic social stress impairs learning in crickets, susceptible and resilient to defeat stress alike. The experience of multiple wins, however, does neither improve nor decrease learning abilities, but a long-term winner effect on aggression could be shown. Although inter-individual differences in learning are present, the aggressive state of crickets is not correlated to the learning indices. The application of serotonergic drugs that block receptors or act as re-uptake inhibitors reveal the influence of serotonin on learning within this paradigm. In addition to maintaining reduced aggressiveness, serotonin promotes the impairment of learning after the experience of chronic social defeat stress.:1 INTRODUCTION.....................................................................................................1 2 MATERIALS AND METHODS............................................................................... 8 2.1 Experimental animals...................................................................................... 8 2.2 Appetitive olfactory conditioning..................................................................... 8 2.2.1 Odour application and rewarding....................................................... 8 2.2.2 Absolute conditioning paradigm........................................................ 10 2.2.3 Differential conditioning paradigm.................................................... 11 2.3 Experimental setup for video-tracking.............................................................. 12 2.4 Binary logistic regression model....................................................................... 13 2.4.1 Binary groups for model building...................................................... 13 2.4.2 Variables of a behavioural response................................................... 14 2.4.3 Calculating a conditioned odour response probability (Presp) ............ 15 2.5 Evaluation of learning with the binary logistic model...................................... 17 2.6 Evaluation of aggression with a standardised fight.......................................... 18 2.7 Multiple agonistic experiences......................................................................... 19 2.7.1 Chronic social defeat stress................................................................ 19 2.7.2 Multiple wins..................................................................................... 20 2.8 Serotonin......................................................................................................... 20 2.8.1 Pharmacological treatments............................................................... 20 2.8.2 Methiothepin and ketanserin.............................................................. 21 2.8.3 Fluoxetine with non-chronic defeat................................................... 21 2.9 Additional data analysis and statistic................................................................ 22 3 RESULTS............................................................................................................ 23 3.1 Binary logistic regression model for quantifying learning............................... 23 3.1.1 Behavioural variables of a conditioned odour response.................... 23 3.1.2 Model building and selection............................................................. 29 3.1.3 Odour response probabilities (Presp)................................................... 31 3.1.4 Application of the regression model to assess the quantification of learning.................................................................................................... 34 3.2 The influence of agonistic experiences on aggression and learning................. 39 3.2.1 Chronic social defeat stress................................................................ 39 3.2.2 Multiple experiences of winning........................................................ 46 3.2.3 Correlation of aggression and learning............................................... 48 3.2.4 Summary of learning capacities – multiple experiences.................... 50 3.3 The influence of serotonergic drugs on learning after chronic defeat.............. 51 3.3.1 Methiothepin and ketanserin.............................................................. 51 3.3.2 Fluoxetine........................................................................................... 57 3.3.3 Summary of learning capacities – chronic defeat and serotonin........ 60 4 DISCUSSION....................................................................................................... 63 4.1 The semi-automated measurement of olfactory learning in individually assayed crickets................................................................................................................... 64 4.2 The influence of multiple agonistic experiences on learning........................... 71 4.3 The role of serotonin in chronic social defeat influenced learning................... 77 4.4 Overall conclusion and outlook........................................................................ 80 5 SUMMARY........................................................................................................... 82 6 ZUSAMMENFASSUNG........................................................................................ 87 7 REFERENCES.................................................................................................... 93 8 APPENDIX................................................................................................... 106 8.1 Figures and tables................................................................................... 106 8.2 Publications and published abstracts....................................................... 108 8.3 Curriculum vitae....................................................................................... 109 8.4 Acknowledgements.................................................................................. 11

Experimental approaches to unravel proximate mechanisms of parasitoid searching and patch leaving behaviour

Animals exploit complex environments in an optimal way, often with limited brain capacities. Interestingly, it is largely unknown, how they do so. This thesis comprises five studies investigating proximate mechanisms modulating the searching behaviour of parasitoid wasps. These organisms serve as excellent organisms for such questions due to their tight link of searching success and fitness. While the first study assumed a simple motor response to serve as a heuristic, yet effective, mechanism, the remaining studies focussed on the role of octopamine [OA] and dopamine [DA]. Both substances being essential in the assessment of reward and aversive stimuli, respectively. Neither the assumed motor response could be met nor did OA or DA reveal any consistent effects with respect to the assessment of rewards and costs. DA slightly impacted the movement pattern. Treatment with OA revealed numerous effects, in total indicating an influence on stress level. Both is in line with studies on other species. Yet, although OA significantly influences searching behaviour, the underlying mechanism is considerably more complex than initially assumed. Last, it was shown that a generalisation on the basis of a few studies and stimuli with respect to the role of OA in the integration of rewards is a simplification

The modelling cycle for collective animal behaviour

Collective animal behaviour is the study of how interactions between individuals produce group level patterns, and why these interactions have evolved. This study has proved itself uniquely interdisciplinary, involving physicists, mathematicians, engineers as well as biologists. Almost all experimental work in this area is related directly or indirectly to mathematical models, with regular movement back and forth between models, experimental data and statistical fitting. In this paper, we describe how the modelling cycle works in the study of collective animal behaviour. We classify studies as addressing questions at different levels or linking different levels, i.e. as local, local to global, global to local or global. We also describe three distinct approaches—theory-driven, data-driven and model selection—to these questions. We show, with reference to our own research on species across different taxa, how we move between these different levels of description and how these various approaches can be applied to link levels together

Individual Scores for Associative Learning in a Differential Appetitive Olfactory Paradigm Using Binary Logistic Regression Analysis

Numerous invertebrates have contributed to our understanding of the biology of learning and memory. In most cases, learning performance is documented for groups of individuals, and nearly always based on a single, typically binary, behavioural metric for a conditioned response. This is unfortunate for several reasons. Foremost, it has become increasingly apparent that invertebrates exhibit inter-individual differences in many aspects of their behaviour, and also that the conditioned response probability for an animal group does not adequately represent the behaviour of individuals in classical conditioning. Furthermore, a binary response character cannot yield a graded score for each individual. We also hypothesise that due to the complexity of a conditioned response, a single metric need not reveal an individual’s full learning potential. In this paper, we report individual learning scores for freely moving adult male crickets (Gryllus bimaculatus) based on a multi-factorial analysis of a conditioned response. First, in an absolute conditioning paradigm, we video-tracked the odour responses of animals that, in previous training, received either odour plus reward (sugar water), reward alone, or odour alone to identify behavioural predictors of a conditioned response. Measures of these predictors were then analysed using binary regression analysis to construct a variety of mathematical models that give a probability for each individual that it exhibited a conditioned response (Presp). Using standard procedures to compare model accuracy, we identified the strongest model which could reliably discriminate between the different odour responses. Finally, in a differential appetitive olfactory paradigm, we employed the model after training to calculate the Presp of animals to a conditioned, and to an unconditioned odour, and from the difference a learning index for each animal. Comparing the results from our multi-factor model with a single metric analysis (head bobbing in response to a conditioned odour), revealed advantageous aspects of the model. A broad distribution of model-learning scores, with modes at low and high values, support the notion of a high degree of variation in learning capacity, which we discuss

Visual homing in field crickets and desert ants: a comparative behavioural and modelling study

Visually guided navigation represents a long standing goal in robotics. Insights may be drawn from various insect species for which visual information has been shown sufficient for navigation in complex environments, however the generality of visual homing abilities across insect species remains unclear. Furthermore variousmodels have been proposed as strategies employed by navigating insects yet comparative studies across models and species are lacking. This work addresses these questions in two insect species not previously studied: the field cricket Gryllus bimaculatus for which almost no navigational data is available; and the European desert ant Cataglyphis velox, a relation of the African desert ant Cataglyphis bicolor which has become a model species for insect navigation studies. The ability of crickets to return to a hidden target using surrounding visual cues was tested using an analogue of the Morris water-maze, a standard paradigm for spatial memory testing in rodents. Crickets learned to re-locate the hidden target using the provided visual cues, with the best performance recorded when a natural image was provided as stimulus rather than clearly identifiable landmarks. The role of vision in navigation was also observed for desert ants within their natural habitat. Foraging ants formed individual, idiosyncratic, visually guided routes through their cluttered surroundings as has been reported in other ant species inhabiting similar environments. In the absence of other cues ants recalled their route even when displaced along their path indicating that ants recall previously visited places rather than a sequence of manoeuvres. Image databases were collected within the environments experienced by the insects using custompanoramic cameras that approximated the insect eye viewof the world. Six biologically plausible visual homing models were implemented and their performance assessed across experimental conditions. The models were first assessed on their ability to replicate the relative performance across the various visual surrounds in which crickets were tested. That is, best performance was sought with the natural scene, followed by blank walls and then the distinct landmarks. Only two models were able to reproduce the pattern of results observed in crickets: pixel-wise image difference with RunDown and the centre of mass average landmark vector. The efficacy of models was then assessed across locations in the ant habitat. A 3D world was generated from the captured images providing noise free and high spatial resolution images asmodel input. Best performancewas found for optic flow and image difference based models. However in many locations the centre of mass average landmark vector failed to provide reliable guidance. This work shows that two previously unstudied insect species can navigate using surrounding visual cues alone. Moreover six biologically plausible models of visual navigation were assessed in the same environments as the insects and only an image difference based model succeeded in all experimental conditions

A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints

Ant homing ability is not diminished when traveling backwards

Ants are known to be capable of homing to their nest after displacement to a novel location. This is widely assumed to involve some form of retinotopic matching between their current view and previously experienced views. One simple algorithm proposed to explain this behavior is continuous retinotopic alignment, in which the ant constantly adjusts its heading by rotating to minimize the pixel-wise difference of its current view from all views stored while facing the nest. However, ants with large prey items will often drag them home while facing backwards. We tested whether displaced ants (Myrmecia croslandi) dragging prey could still home despite experiencing an inverted view of their surroundings under these conditions. Ants moving backwards with food took similarly direct paths to the nest as ants moving forward without food, demonstrating that continuous retinotopic alignment is not a critical component of homing. It is possible that ants use initial or intermittent retinotopic alignment, coupled with some other direction stabilizing cue that they can utilize when moving backward. However, though most ants dragging prey would occasionally look toward the nest, we observed that their heading direction was not noticeably improved afterwards. We assume ants must use comparison of current and stored images for corrections of their path, but suggest they are either able to chose the appropriate visual memory for comparison using an additional mechanism; or can make such comparisons without retinotopic alignment