Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behaviour

Animal behaviour is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioural data. Here, we develop computational tools and study the behaviour of hundreds of larval zebrafish tracked continuously across multiple 24-hour day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva’s behaviour to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioural patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organisation of larval zebrafish behaviour at multiple timescales and provides tools to identify structure from large-scale behavioural datasets.

Interpolation of multidimensional signals using the reduction of the dimension of parametric spaces of decision rules

In this paper, we consider the interpolation of multidimensional signals problem. We develop adaptive interpolators that select the most appropriate interpolating function at each signal point. Parameterized decision rule selects the interpolating function based on local features at each signal point. We optimize the adaptive interpolator in the parameter space of this decision rule. For solving this optimization problem, we reduce the dimension of the parametric space of the decision rule. Dimension reduction is based on the parameterization of the ratio between local differences at each signal point. Then we optimize the adaptive interpolator in parametric space of reduced dimension. Computational experiments to investigate the effectiveness of an adaptive interpolator are conducted using real-world multidimensional signals. The proposed adaptive interpolator used as a part of the hierarchical compression method showed a gain of up to 51% in the size of the archive file compared to the smoothing interpolator.

Adaptive interpolation of multidimensional signals for compression on board an aircraft

We consider the compression of multidimensional signals on the aircraft board. We describe the data of such signals as a hypercube, which is "rotated" in a special way. To compress this hypercube, we use a hierarchical compression method. As one of the stages of this method, we use an adaptive interpolation algorithm. The adaptive algorithm automatically switches between different interpolating functions at each signal point. We perform computational experiments in real-world multidimensional signals. Computational experiments confirm that the use of proposed adaptive interpolator allows increasing (up to 31%) the compression ratio of the “rotated” hypercube corresponding to multidimensional hyperspectral signals.

Interpolation based on context modeling for hierarchical compression of multidimensional signals

Context algorithms for interpolation of multidimensional signals in the compression problem are researched. A hierarchical compression method for arbitrary dimension signals is considered. For this method, an interpolation algorithm based on the context modeling is proposed. The algorithm is based on optimizing parameters of the interpolating function in a local neighborhood of the interpolated sample. At the same time, locally optimal parameters found for more decimated scale signal levels are used to interpolate samples of less decimated scale signal levels. The context interpolation algorithm is implemented programmatically as part of a hierarchical compression method. Computational experiments have shown that using a context interpolator instead of an average interpolator makes it possible to significantly improve the efficiency of hierarchical compression.