Faculty of Technology: News - Kategorie Forschung
Professor Dr. Ulrich Rückert, a researcher at Bielefeld University’s Cluster of Excellence CITEC and the Faculty of Technology, is working to better understand the human brain with the help of computer-based models. This project is taking place throughout the European Union – the goal of which is to gather findings on the brain and make new connections with this information.
The HBP is set to run for total of ten years, until 2023. The project is divided into different subphases: the third (and current) phase will run from 2018–2020, and the fourth phase is planned for 2020–2023. With each phase, project groups are required to submit proposals for continued funding. This project, which Bielefeld University is contributing to, has been evaluated as “very good” and a total of 1.19 billion Euro has been budgeted for the EU project.
“We have, relatively speaking, a lot of detailed knowledge about the brain,” says Rückert, who heads the “Cognitronics and Sensor Systems” research group at Bielefeld University’s Faculty of Technology. This research group is also part of the Cluster of Excellence Cognitive Interaction Technology (CITEC) at Bielefeld University.
It is clear, for instance, that nerve cells interact via electrical impulses. “In addition to this, the functions of individual regions of the brain are also known,” says Rückert. But it is not clear how the molecular level of the brain relates to its larger structures. “In this area, there’s still a big gap,” says the engineer.
The goal of the overarching project is to gather knowledge about the human brain and forge connections with this information. This knowledge is meant to serve the fields of medicine and computer science in particular: if researchers succeed in simulating the brain as accurately as possible, neurological diseases such as Alzheimer’s or Parkinson’s, for instance, could be better understood – or perhaps even cured. The efficacy and side effects of medications could also be tested with computer-based models.
In HBP, Ulrich Rückert deals with associative memory, conducting research on neuronal networks. “This is about developing a new architecture for computers,” he says. Computers can make things incredibly efficient, such as playing chess or solving computational tasks. “They are always good when there are set rules and structures,” says Rückert.
The human brain, however, works in a completely different way: it is very good at putting different things together in relationships. “When we go into a room, for example, we know right away where we are and where we are located in the space,” says Rückert. “For a computer, on the other hand, this would be a very complicated computational operation.”
The brain operates under very different principles than a computer does. “A computer often needs 1000x more energy than the brain for such operations,” says Rückert. “If we can simulate how the brain works, then the energy usage of technical systems could be reduced in many areas.” This applies to very different systems in robotics, such as autonomous driving. “Energy supply has thus far been a major problem with this,” says Rückert. As he explains, “one solution could be having certain processes such as orientation run more energy efficiently.”
The brain is not particularly fast, but it is efficient in using energy. “For a single operation, a computer is faster” says Rückert. “But a big strength of the brain is that different processes run in parallel and are linked with each another.” In HBP, the models for this are primarily virtual, but they are also replicated in analog form.
Currently, Rückert has been given the task of assessing the neuronal models from two research groups in Heidelberg and Mannheim. “We are working closely together and are having intensive exchange with the individual groups in the project” says Rückert. “I’m really looking forward to the results.”
Prof. Dr.-Ing. Ulrich Rückert, Bielefeld University
Cluster of Excellence Cognitive Interaction Technology (CITEC) / Faculty of Technology
Telephone: 0521 106-12050
Bumblebees have a small brain but they cover considerable distance in the search for food. Depending on the species, their radius is up to three kilometres. The flight route is teeming with enemies and obstacles. Changing wind speeds and wind directions add to the hazards. The insects have to steer their flight through a changeable environment, navigate extensively and learn how to find a good source of food and get home to their nests.
"When bumblebees leave their nest for the first time, they take flights to learn their surroundings so that they can find their way back," says Dr. Olivier Bertrand from the Department of Neurobiology at Bielefeld University, Germany. "These flights have a loop-like pattern, whereby the pattern varies from animal to animal, as our studies show. We assume that the bumblebees store snapshots of their environment in their brain, the usefulness of which is checked on subsequent flights."
When flying within a complex cluttered environment, bees constantly need to evaluate the environmental features and have to make decisions that influence the flight course. Dr. Shridar Ravi from the RMTI University in Melbourne, Australia, used bumblebees to seek insights into the mechanisms used for gap identification when the bees are confronted with an obstacle in their flight path and have to assess gap properties. Bees spend significant time in the near vicinity of the gap while performing rapid lateral maneuvers and looking at the gap, as if they would scan the gap to collect important information. In doing so the bee could detect the edges of the gap by utilising the difference between the relative motion of the gap edges and the foreground or background: a closer object moves relative faster than objects in the background.
As long as the capabilities of robots are limited, linking the abilities of animals with those of robots could be helpful. The team led by Prof. Dr. Noriyasu Ando from the Research Center for Advanced Science and Technology in Tokyo has taken this path: they have developed an insect-driven mobile robot. "A male silkmoth sits in a cockpit and his walking controls the robot
and directs it to a female moth as soon as he notices her sexual pheromone and reacts to it", is how Professor Ando describes the principle.
"From a technical point of view, this hybrid robot’s performance matches our goal: the future insect mimetic robot will have the model of the insect brain." The hybrid robot also provides scientists with insights into the behaviour of insects. By changing the sensory input and/or the motor output of the robot, the team was able to uncover the sensory-motor control of the reactions of silkmoths to odours. "The hybrid robot enables us to compare an insect brain with an electronic model," said Professor Ando. "Now the robot is controlled directly by a real silkmoth. If the insect is replaced by a robot model of this insect, we can directly compare the performance of the insect brain with that of the model brain on this robot platform. It's still a conceptual idea, but we're working on it.”
"We have developed a new electronic motion detector, the “Spiking Elementary Motion Detector”, which can detect the relative motion of objects”, says Professor Chicca. Every car or train driver knows what a "relative movement" is: the church tower in the distance glides slowly past, while the tree at the roadside rushes very quickly past. Insects use such information during navigation in the terrain to avoid collisions.
The new motion detector, sEMD for short, is a technical nerve cell with an artificial synapse. It can pick up signals and produce signals when two pulses arrive within a certain time - hence the name suffix "spiking". A chip can carry thousands of these detectors, depending on the experiment.
The detectors receive their input from innovative neuromorphic cameras, developed by a company in switzerland. In contrast to normal cameras, the pixels of the sensors in these cameras only produce a signal independently if something changes in their "field of vision". These signals are picked up by the motion detector's receptive fields. Each detector has two receptive fields, each receiving signals from nine pixels. If more than half of the pixels of a receptive field are activated, the receptive field produces a signal that is further processed by the detector. The detector can calculate the relative speed at which an object moves in front of the camera based on the time intervals between the signals of two adjacent receptive fields. "Our experiments show that it is possible to generate information for the navigation of robots that avoid collisions," explains Professor Chicca. "Our results pave the way for the construction of low-power compact systems for autonomous navigation. In addition, the sEMD is a universally applicable element for calculating time differences and can therefore also be used for processing other sensory stimuli, for example for locating the source of a sound.
Dr. Olivier Bertrand
Universität Bielefeld, Neurobiologie
Prof. Dr. Elisabetta Chicca
Universität Bielefeld, AG Neuromorphic Behaving Systems
A team of students and researchers from the Cluster of Excellence Cognitive Interaction Technology (CITEC) at Bielefeld University won the RoboCup World Championship in Montreal, Canada. RoboCup is the leading, and largest, competition for intelligent robots in the world. The “Team of Bielefeld” (ToBi) showed its skills with Pepper the robot in the household service league. More than 400 teams from around the world competed in the various leagues of the competition from 18-22 June 2018. The researchers are now back in Bielefeld.
Lier adds: “The team prepared itself very well, also for dealing with uncertainties. The infrastructure there is different from that in the lab. The students put a lot of work into making the software as stable as possible, and they succeeded in this.”
In the household service league RoboCup@Home, their robot had to master various assistive tasks as precisely as possible, including working as a waiter, bringing groceries into the home, loading a dishwasher, giving visitors an introductory tour of RoboCup, and finding its way in unfamiliar surroundings. The CITEC team competed in the Social Standard Platform League (SSPL), a subleague of the household service league. In the SSPL, teams only compete with Pepper, a robot produced by the company Softbank. Second place went to the team from Australia, where the next RoboCup competition will be held, and the team from Chile took third place.
Student Janneke Simmering from the CITEC team took part in the robot world championship for the first time. “The exciting question was: will the robot do what it’s supposed to do? We spent four weeks programming the software and tried to prepare for as many factors and eventualities as possible. The work paid off, and that’s a great feeling. We’re celebrating now.”
Members of this year’s team included: Robert Feldhans, Felix Friese, Kai Konen, David Leins, Jan Patrick Nülle, Sarah Schröder, Janneke Simmering, Philipp von Neumann-Cosel, Johannes Kummert, Florian Lier and Sven Wachsmuth. The preparations for RoboCup are incorporated into a university seminar – each year, new students from the course work together in the team. The Cluster of Excellence Cognitive Interaction Technology (CITEC) has participated in RoboCup since 2009. In 2016, the team earned the title of world champion for the first time, and the team has also taken third place a total of three times: 2012, in Mexico; 2015, in China; and 2017, in Japan.
The Cluster of Excellence Cognitive Interaction Technology (CITEC) at Bielefeld University is one of 43 clusters of excellence in Germany, and the only cluster with a focus in robotics. CITEC is working to make technical systems intuitive and easy to operate. CITEC’s interdisciplinary approach combines cognitive research with technology. Since 2007, CITEC has been funded as part of the Excellence Initiative of the German federal and state governments. Approximately 250 researchers work at the Cluster.
Professor Dr. Ellen Baake (56) has been appointed for five years to the Board of the European Society of Mathematical and Theoretical Biology (European Society of Mathematical and Theoretical Biology, ESMTB). Task of the ESMTB is the promotion of theoretical approaches and mathematical methods in the Life sciences worldwide and in Europe. The society organizes and supports conferences and summer schools on an international level and in 2018 co-ordinates diverse activities for the "Year of Mathematical Biology ". Ellen Baake leads the working group Biomathematics and Theoretical Bioinformatics at the Faculty of Engineering the University of Bielefeld. Since 2006 she is the spokeswoman for Research Center Mathematical Modeling and since 2011 coordinated the the priority program "Probabilistic Structures in Evolution "of the German Research Foundation (DFG).
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|« March 2019|