Tim Landgraf

Sleep deprivation affects extinction but not acquisition memory in honeybees

Sleep-like behavior has been studied in honeybees before, but the relationship between sleep and memory formation has not been explored. Here we describe a new approach to address the question if sleep in bees, like in other animals, improves memory consolidation. Restrained bees were observed by a web camera, and their antennal activities were used as indicators of sleep. We found that the bees sleep more during the dark phase of the day compared with the light phase. Sleep phases were characterized by two distinct patterns of antennal activities: symmetrical activity, more prominent during the dark phase; and asymmetrical activity, more common during the light phase. Sleep-deprived bees showed rebound the following day, confirming effective deprivation of sleep. After appetitive conditioning of the bees to various olfactory stimuli, we observed their sleep. Bees conditioned to odor with sugar reward showed lesser sleep compared with bees that were exposed to either reward alone or air alone. Next, we asked whether sleep deprivation affects memory consolidation. While sleep deprivation had no effect on retention scores after odor acquisition, retention for extinction learning was significantly reduced, indicating that consolidation of extinction memory but not acquisition memory was affected by sleep deprivation.

RoboFish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies.

In recent years, simple biomimetic robots have been increasingly used in biological studies to investigate social behavior, for example collective movement. Nevertheless, a big challenge in developing biomimetic robots is the acceptance of the robotic agents by live animals. In this contribution, we describe our recent advances with regard to the acceptance of our biomimetic RoboFish by live Trinidadian guppies (Poecilia reticulata). We provide a detailed technical description of the RoboFish system and show the effect of different appearance, motion patterns and interaction modes on the acceptance of the artificial fish replica. Our results indicate that realistic eye dummies along with natural motion patterns significantly improve the acceptance level of the RoboFish. Through the interactive behaviors, our system can be adjusted to imitate different individual characteristics of live animals, which further increases the bandwidth of possible applications of our RoboFish for the study of animal behavior.

Analysis of the waggle dance motion of honeybees for the design of a biomimetic honeybee robot

The honeybee dance “language” is one of the most popular examples of information transfer in the animal world. Today, more than 60 years after its discovery it still remains unknown how follower bees decode the information contained in the dance. In order to build a robotic honeybee that allows a deeper investigation of the communication process we have recorded hundreds of videos of waggle dances. In this paper we analyze the statistics of visually captured high-precision dance trajectories of European honeybees (Apis mellifera carnica). The trajectories were produced using a novel automatic tracking system and represent the most detailed honeybee dance motion information available. Although honeybee dances seem very variable, some properties turned out to be invariant. We use these properties as a minimal set of parameters that enables us to model the honeybee dance motion. We provide a detailed statistical description of various dance properties that have not been characterized before and discuss the role of particular dance components in the commmunication process.

Blending in with the Shoal: Robotic Fish Swarms for Investigating Strategies of Group Formation in Guppies

Robotic fish that dynamically interact with live fish shoals dramatically augment the toolset of behavioral biologists. We have developed a system of biomimetic fish for the investigation of collective behavior in Guppies and similarly small fish. This contribution presents full implementation details of the system and promising experimental results. Over long durations our robots are able to integrate themselves into shoals or recruit the group to exposed locations that are usually avoided. This system is the first open-source project for both software and hardware components and is supposed to facilitate research in the emerging field of bio-hybrid societies.

A biomimetic honeybee robot for the analysis of the honeybee dance communication system

A new biomimetic honeybee robot capable of dancing and mimicking all known signals in the honeybee dance communication system has been built. This paper describes the hard- and software design of the first honeybee robot with computer vision. The robot can robustly recognize obstacles and react on imminent collisions.

RenderGAN: Generating Realistic Labeled Data

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

Conditioned behavior in a robot controlled by a spiking neural network

Insects show a rich repertoire of goal-directed and adaptive behaviors that are still beyond the capabilities of todays artificial systems. Fast progress in our comprehension of the underlying neural computations make the insect a favorable model system for neurally inspired computing paradigms in autonomous robots. Here, we present a robotic platform designed for implementing and testing spiking neural network control architectures. We demonstrate a neuromorphic realtime approach to sensory processing, reward-based associative plasticity and behavioral control. This is inspired by the biological mechanisms underlying rapid associative learning and the formation of distributed memories in the insect.

Design and development of a robotic bee for the analysis of honeybee dance communication

We have designed a robotic honeybee to mimic the bee dance communication system. To achieve this goal, a tracking system has been developed to extract real bee dance trajectories recorded with high-speed video cameras. The results have been analysed to find the essential properties required for the prototype robot. Putative signals in the dance communication have been identified from the literature. Several prototypes were built with successive addition of more features or improvement of existing components. Prototypes were tested in a populated beehive results were documented using high-speed camera recordings. A substantial innovation is a visual feedback system that helps the robot to minimise collisions with other bees.

Interactive Robotic Fish for the Analysis of Swarm Behavior

Biomimetic robots can be used to analyze social behavior through active interference with live animals. We have developed a swarm of robotic fish that enables us to examine collective behaviors in fish shoals. The system uses small wheeled robots, moving under a water tank. The robots are coupled to a fish replica inside the tank using neodymium magnets. The position of the robots and each fish in the swarm is tracked by two cameras. The robots can execute certain behaviors integrating feedback from the swarm’s position, orientation and velocity. Here, we describe implementation details of our hardware and software and show first results of the analysis of behavioral experiments.

Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as durations or angles are extracted manually directly from the observation hive or from video recordings to quantify dance properties, particularly to determine where bees have foraged. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. As a further step, we have developed technologies to track all individuals of a honeybee colony and detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition and decoding of tags and dances, and we discuss potential research directions that may benefit from automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses for a continuous, experimental recording of nine weeks duration.