A Computational Analysis of the Gradient Navigation strategies of the Nematode Caenorhabditis elegans.

In the present thesis, we apply computational methods to the study of animal behaviour. Specifically, we are interested in the gradient navigation strategies of C. elegans, for which we show that there are many interesting questions that have not yet been answered by existing research. In order to study the behaviour of C. elegans, we first develop a range of tools to help us do so. We base a large part of our work on Markov-like models of behaviour and since these are not Markovian in the strict sense (limiting the analytical tools which can be used to study their behaviour), we first present a possible transformation from a Markov-like model with variable transition probabilities into a strictly Markovian model. We next present a framework for studying the behaviour of behavioural models which is not restricted to the work presented here but is likely to find general use in behavioural studies. Using these tools, we then analyse the chemotactic behaviour of C. elegans, showing that we can adequately explain most features of this behaviour using energy-efficiency considerations. We also show that the main behavioural strategy, so-called pirouettes is likely to be caused by an inability to sample the environment during a turn and that the animal my not be acting upon gradient information while reversing. Finally, we investigate the deterministic isotherm tracking strategy displayed by C. elegans. We develop a computational model for this behaviour which is able to reproduce all of the main features of C. elegans isotherm tracking and we propose a candidate neural circuit which might encode this strategy. Additionally, we briefly discuss the use of stochastic strategies by the animal when moving towards its preferred temperature. In summary, the work presented here therefore provides contributions to two major fields: we extend the methodology available for behavioural analysis in ethology and we contribute a number of insights and advancements to the field of C. elegans research

Difference between the regression index of networks produced using the 25 initial states of the social condition with regular prior reliance (<i>H</i>_prior = 1) and the regression index produced with the same initial states using increased (<i>H</i>_prior < 1) prior reliance.

(a) For all ten networks the median of the subject-wise difference is displayed. Horizontal lines mark the zero line, the average subject-wise difference in the regression index between the social and the mechanical condition in human data, and the average subject-wise difference in regression index between the social and the individual condition. (b) Detailed results including all subject data for a single network. The subject-wise differences between the behavior using social initial states of H = 1 vs. H = x for different x values is displayed.</p

Explanation of how the pairwise distances across participants were computed from the neural activation traces.

Each circle represents one trajectory of 25 × 22 where 25 is the number of neurons and 22 is the number of time steps. Data is split into 11 length categories and the pairwise distances within conditions are computed for each length category individually and later averaged, such that differences between lengths do not affect the final measure. The final measure, thus, shows for each time step the average distance between participants (cf. Fig 9).</p

The reproduced lengths plotted against the presented lengths, where lengths were calculated in the normalized space of trajectories.

Original human data (left) is compared with the corresponding mean predictions produced by one example network (right) for six randomly chosen participants. Lines in both plots correspond to the regression lines extracted from the human data or the model data, respectively. The black line shows the identity line.</p

Subject-wise differences between different conditions, compared for human data (black) and model data (magenta) for one trained example network.

Boxes indicate the mean, and 80% confidence intervals of the data, fliers indicate standard deviation. Model data reproduce the main trends of the data, but with slightly lower variability. The p-values were computed using the results of all ten networks, i.e. on 25 samples from the human participants, and 250 (= 10 ⋅ 25) samples from the models.</p

The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general