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Berlin Biorobotics
Research in Collective Intelligence, Machine Learning and Biorobotics at Free University Berlin
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Project

BeesBook: Analysis of Social Networks in Honeybee Colonies

October 11, 2012Tim Landgraf

Bees have amazing cognitive capabilities. The scouts explore and learn the terrain and build up a highly detailed neuronal representation of the surrounding environment. Once back in the hive, locations are communicated to others by translating location information into body… Continue Reading →

Project

NeuroCopter: Biomimetic Flying Platform for the Investigation of Honeybee Navigation

October 11, 2012Tim Landgraf

NeuroCopter is a joint project of the Biorobotics Lab and the Neuroinformatics group at Free University Berlin. Our goal is to control an autonomously flying robot with a brain simulation. We want to develop the neural control architecture for “high-level” tasks such as learning, memory and navigation.… Continue Reading →

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RoboFish: Robotic Fish Swarms for the Analysis of Collective Behavior

October 11, 2012Tim Landgraf

Suppose a school of fishes suddenly turns. Who initated this turn? Was this leading fish acting first because of the topology of the swarm, him being closest to the cue? Or do individuals exist with a higher probability of leading?… Continue Reading →

Project

RoboBee: Analysis of the Honeybee Dance Communication System

October 11, 2012Tim Landgraf

Upon her return from a foraging flight a bee might behave quite strangely: she throws her body from side to side moving wildly over the comb surface. Others attend this performance and – after a little while – show up… Continue Reading →

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BioTracker: An Open-Source Video Tracking Framework for the Analysis of Animal Behavior

October 11, 2012Tim Landgraf

We are close to publishing a C++ framework for the development of computer vision tracking systems. Stay tuned!

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  • Paper Discussion: Why Normalizing Flows Fail to Detect Out-of-Distribution Data.
  • Paper Discussion: World Models
  • Paper Discussion: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
  • Paper Discussion: When Explanations Lie: Why Modified BP Attribution Fails
  • Paper Discussion: DeepProbLog: Neural Probabilistic Logic Programming

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