Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions

Soon after hatching, the young of precocial species, such as domestic chicks or ducklings, learn to recognize their social partner by simply being exposed to it (imprinting process). Even artificial objects or stimuli displayed on monitor screens can effectively trigger filial imprinting, though learning is canalized by spontaneous preferences for animacy signals, such as certain kinds of motion or a face-like appearance. Imprinting is used as a behavioural paradigm for studies on memory formation, early learning and predispositions, as well as number and space cognition, and brain asymmetries. Here, we present an automatized setup to expose and/or test animals for a variety of imprinting experiments. The setup consists of a cage with two high-frequency screens at the opposite ends where stimuli are shown. Provided with a camera covering the whole space of the cage, the behaviour of the animal is recorded continuously. A graphic user interface implemented in Matlab allows a custom configuration of the experimental protocol, that together with Psychtoolbox drives the presentation of images on the screens, with accurate time scheduling and a highly precise framerate. The setup can be implemented into a complete workflow to analyse behaviour in a fully automatized way by combining Matlab (and Psychtoolbox) to control the monitor screens and stimuli, DeepLabCut to track animals’ behaviour, Python (and R) to extract data and perform statistical analyses. The automated setup allows neuro-behavioural scientists to perform standardized protocols during their experiments, with faster data collection and analyses, and reproducible results.

Tinbergen’s challenge for the neuroscience of behavior

Nobel laureate Nikolaas Tinbergen provided clear criteria for declaring a neuroscience problem solved, criteria which despite the passage of more than 50 years and vastly expanded neuroscience tool kits remain applicable today. Tinbergen said for neuroscientists to claim that a behavior is understood, they must correspondingly understand its (i) development and its (ii) mechanisms and its (iii) function and its (iv) evolution. Now, all four of these domains represent hotbeds of current experimental work, each using arrays of new techniques which overlap only partly. Thus, as new methodologies come online, from single-nerve-cell RNA sequencing, for example, to smart FISH, large-scale calcium imaging from cortex and deep brain structures, computational ethology, and so on, one person, however smart, cannot master everything. Our response to the likely “fracturing” of neuroscience recognizes the value of ever larger consortia. This response suggests new kinds of problems for (i) funding and (ii) the fair distribution of credit, especially for younger scientists.

An open source platform for analyzing and sharing worm behavior data

Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists1. However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.