More than 60 years after the discovery of the meaning of the dance many questions remain unanswered. How do the follower bees decode the dance? Which stimuli sent out by the dancer carry information and which are actually used by the receivers of the message?

Therefor, the research groups of Prof. Dr. Menzel (Institute of Neurobiology) and Prof. Dr. Rojas (Institute of Computer Science) are studying the dance communication in Apis mellifera bees by means of a robotic honeybee, aka the Project RoboBee.

Using a biomimetic fish robot we are able to test various hypotheses: we can make the Robofish thin or big, act risk-averse or adventurous, nervous or calm. Having full control over the hypothesis, experiments are perfectly reproduceable.

In the project RoboFish, as a joint project of Freie Universität Berlin (Rojas group) and Leibniz Institute of Freshwater Ecology (Krause group), we are developing a biomimetic fish swarm for the investigation of swarm intelligence in fish schools.

The robot will learn to navigate in previously unknown terrain by relying on sensory modalities of bees, i.e. measurements of stereoscopic optical flow, polarization of the sky, color and geometrical terrain features and odor detection. The central brain of the robot is equipped with a spiking neural network of deep architecture where learning is expressed locally by different forms of synaptic plasticity and metaplasticity, reinforced through external and internal rewards. The central network will compute behavioral decisions that drive low level motor plans.

The first version of the robot will rely on simulated neural circuitry; the final version will be equipped with neuromorphic electronics running a network of spiking neurons in hardware structures.

In the past, answering those questions was very laborious. Biologists would sit in front of an observation hive, keeping track of single marked bees and take note with whom they dance. We are taking this analysis to the next level. We develop a system which allows tracking every single individual inside the hive. We use unique tags to mark each bee and develop a machine vision system to find every bee in the hive. We can then tell which bees are communicating one with whom and where and how long and so on. The analysis of the complete social network has never been done previously. We are happy to work with our partner Zuse Institut Berlin where all our data (~190 Terabyte) is stored and analyzed on supercomputers.