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Contains fulltext : 295428.pdf (Publisher’s version ) (Open Access), Automated observation and analysis of behavior is important to facilitate progress in many fields of science. Recent developments in deep learning have enabled progress in object detection and tracking, but rodent behavior recognition struggles to exceed 75-80% accuracy for ethologically relevant behaviors. We investigate the main reasons why and distinguish three aspects of behavior dynamics that are difficult to automate. We isolate these aspects in an artificial dataset and reproduce effects with the state-of-the-art behavior recognition models. Having an endless amount of labeled training data with minimal input noise and representative dynamics will enable research to optimize behavior recognition architectures and get closer to human-like recognition performance for behaviors with challenging dynamics.

Contains fulltext : 252375.pdf (Publisher’s version ) (Open Access)

Measuring Behavior 2022: : 12th International Conference on Methods and Techniques in Behavioral Research, and 6th Seminar on Behavioral Methods (May 18-20, 2022), Contains fulltext : 283560.pdf (Publisher’s version ) (Open Access)

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Rede uitgesproken bij de aanvaarding van het ambt van hoogleraar Behavior, Information Technology and Innovation aan de Faculteit der Natuurwetenschappen, Wiskunde en Informatica, 08 oktober 2021, Contains fulltext : 245184.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 239279.pdf (Publisher’s version ) (Open Access), The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more "natural" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus PhenoTyper® home-cages and the video-based tracking software, EthoVision® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a "home-cage", we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new develop

Contains fulltext : 234287.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, In this chapter, we address how one can detect and measure specific behaviors in zebrafish, from the embryonic stage to adulthood, and in various behavioral paradigms using automated, image-based analysis software. In the first section, we address the measurement of activity in embryos and heart rate in larvae. For the larval stage, we also show how software can analyze the fish's body shape and movement trajectory to measure activity and specific behaviors like avoidance and escape responses. Custom output variables address specific research questions and increase throughput. For adult zebrafish, we describe applications with the novel tank diving test, learning in the T-maze, aggressive behavior in mirror tests, and quantify shoaling behavior. The advantages of tracking in three dimensions relative to classic 2D tracking are also addressed. Finally, we discuss current limitations and directions for future development.

Contains fulltext : 214277.pdf (publisher's version ) (Open Access), Automated observation and analysis of rodent behavior is important to facilitate research progress in neuroscience and pharmacology. Available automated systems lack adaptivity and can benefit from advances in AI. In this work we compare a state-of-the-art conventional rat behavior recognition (RBR) system to an advanced deep learning method and evaluate its performance within and across experimental setups. We show that using a multi-fiber network (MF-Net) in conjunction with data augmentation strategies within-setup dataset performance improves over the conventional RBR system. Two new methods for video augmentation were used: video cutout and dynamic illumination change. However, we also show that improvements do not transfer to videos in different experimental setups, for which we discuss possible causes and cures.

Contains fulltext : 225123.pdf (publisher's version ) (Closed access), Recently, a petition was offered to the European Commission calling for an immediate ban on animal testing. Although a Europe-wide moratorium on the use of animals in science is not yet possible, there has been a push by the non-scientific community and politicians for a rapid transition to animal-free innovations. Although there are benefits for both animal welfare and researchers, advances on alternative methods have not progressed enough to be able to replace animal research in the foreseeable future. This trend has led first and foremost to a substantial increase in the administrative burden and hurdles required to make timely advances in research and treatments for human and animal diseases. The current COVID-19 pandemic clearly highlights how much we actually rely on animal research. COVID-19 affects several organs and systems, and the various animal-free alternatives currently available do not come close to this complexity. In this Essay, we therefore argue that the use of animals is essential for the advancement of human and veterinary health.

Measuring Behavior 2018: 11th International Conference on Methods and Techniques in Behavioral Research, Manchester Metropolitan University, 5th-8th June 2018, Item does not contain fulltext

Item does not contain fulltext, Background: Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. New method: To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. Results: We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Comparison with existing methods: Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. Conclusions: With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings.

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece, 19-23 March 2018, Item does not contain fulltext, Egocentric vision is a technology that exists in a variety of fields such as life-logging, sports recording and robot navigation. Plenty of research work focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that locations can be characterized by the presence of specific objects. Our objective is the recognition of locations in egocentric videos that mainly consist of indoor house scenes. We perform an extensive comparison between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based classification methods that aim at finding the in-house location by classifying the detected objects which are extracted with a state-of-the-art object detector. We show that location classification is affected by the quality of the detected objects, i.e., the false detections among the correct ones in a series of frames, but this effect can be greatly limited by taking into account the temporal structure of the information by using LSTM. Finally, we argue about the potential for useful real-world applications.

Background: Piercing-sucking insects are major vectors of plant viruses causing significant yield losses in crops.Functional genomics of plant resistance to these insects would greatly benefit from the availability of highthroughput, quantitative phenotyping methods. Results: We have developed an automated video tracking platform that quantifies aphid feeding behaviour on leaf discs to assess the level of plant resistance. Through the analysis of aphid movement, the start and duration of plant penetrations by aphids were estimated. As a case study, video tracking confirmed the near-complete resistance of lettuce cultivar ‘Corbana’ against Nasonovia ribisnigri (Mosely), biotype Nr:0, and revealed quantitative resistance in Arabidopsis accession Co-2 against Myzus persicae (Sulzer). The video tracking platform was benchmarked against Electrical Penetration Graph (EPG) recordings and aphid population development assays. The use of leaf discs instead of intact plants reduced the intensity of the resistance effect in video tracking, but sufficiently replicated experiments resulted in similar conclusions as EPG recordings and aphid population assays. One video tracking platform could screen 100 samples in parallel. Conclusions: Automated video tracking can be used to screen large plant populations for resistance to aphids and other piercing-sucking insects.