Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Computing with silicon neurons

28.01.2014
Scientists from Berlin and Heidelberg use artifical nerve cells to classify different types of data. Thus, they may recognize handwritten numbers, or distinguish plant species based on their flowers.

A bakery assistant who takes the bread from the shelf just to give it to his boss who then hands it over to the customer? Rather unlikely. Instead, both work at the same time to sell the baked goods. Similarly, computer programs are more efficient if they process data in parallel rather than to calculate them one after the other. However, most programs that are applied still work in a serial manner.


The neuromorphic chip containing silicon neurons which the researchers used for their data-classifying network.

Kirchhoff Institute for Physics, Heidelberg University

Scientists from the Freie Universität Berlin, the Bernstein Center Berlin and Heidelberg University have now refined a new technology that is based on parallel data processing. In the so-called neuromophic computing, neurons made of silicon take over the computational work on special computer chips. The neurons are linked together in a similar fashion to the nerve cells in our brain. If the assembly is fed with data, all silicon neurons work in parallel to solve the problem. The precise nature of their connections determines how the network processes the data. Once properly linked, the neuromorphic network operates almost by itself. The researchers have now designed a network–a neuromorphic “program”–for this chip that solves a fundamental computing problem: It can classify data with different features. It is able to recognize handwritten numbers, or may distinguish certain plant species based on flowering characteristics.

"The design of the network architecture has been inspired by the odor-processing nervous system of insects," explains Michael Schmuker, lead author of the study. "This system is optimized by nature for a highly parallel processing of the complex chemical world." Together with work group leader Martin Nawrot and Thomas Pfeil, Schmuker provided the proof of principle that a neuromorphic chip can solve such a complex task. For their study, the researchers used a chip with silicon neurons, which was developed at the Kirchhoff Institute for Physics of Heidelberg University.

Computer programs that can classify data are employed in various technical devices, such as smart phones. The neuromorphic network chip could also be applied in super-computers that are built on the model of the human brain to solve very complex tasks. Using their prototype, the Berlin scientists are now able to explore how networks must be designed to meet the specific requirements of these brain-like computer. A major challenge will be that not even two neurons are identical – neither in silicon nor in the brain.

The Bernstein Center Berlin is part of the National Bernstein Network Computational Neuroscience in Germany. With this funding initiative, the German Federal Ministry of Education and Research (BMBF) has supported the new discipline of Computational Neuroscience since 2004 with over 170 million Euros. The network is named after the German physiologist Julius Bernstein (1835-1917).

Contact:
Dr. Michael Schmuker
Freie Universität Berlin
Institute of Biology – Neurobiology
Königin-Luise-Straße 1-3, room 205
14195 Berlin 

Tel: +49 (0)30 838 57294
Email: m.schmuker@fu-berlin.de
Original publication:
M. Schmuker, T. Pfeil & M.P. Nawrot (2014): A neuromorphic network for generic multivariate data classification. PNAS, published ahead of print January 27, doi:10.1073/pnas.1303053111

http://www.pnas.org/cgi/doi/10.1073/pnas.1303053111

Weitere Informationen:

http://biomachinelearning.net personal website Michael Schmuker
http://fu-berlin.de/neuroinformatik Martin Paul Nawrot’s lab
https://www.bccn-berlin.de Bernstein Center Berlin
http://www.fu-berlin.de Freie Universität Berlin
http://www.kip.uni-heidelberg.de/cms/groups/vision Electronic Vision(s), Heidelberg University

http://www.nncn.de National Bernstein Network Computational Neuroscience

Mareike Kardinal | idw
Further information:
http://www.nncn.de

More articles from Information Technology:

nachricht Magnetic fields provide a new way to communicate wirelessly
01.09.2015 | University of California - San Diego

nachricht 'Magic' sphere for information transfer
24.08.2015 | Lomonosov Moscow State University

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: Hubble survey unlocks clues to star birth in neighboring galaxy

In a survey of NASA's Hubble Space Telescope images of 2,753 young, blue star clusters in the neighboring Andromeda galaxy (M31), astronomers have found that M31 and our own galaxy have a similar percentage of newborn stars based on mass.

By nailing down what percentage of stars have a particular mass within a cluster, or the Initial Mass Function (IMF), scientists can better interpret the light...

Im Focus: Fraunhofer ISE Develops Highly Compact Inverter for Uninterruptible Power Supplies

Silicon Carbide Components Enable Efficiency of 98.7 percent

Researchers at the Fraunhofer Institute for Solar Energy Systems ISE have developed a highly compact and efficient inverter for use in uninterruptible power...

Im Focus: How wind sculpted Earth's largest dust deposit

China's Loess Plateau was formed by wind alternately depositing dust or removing dust over the last 2.6 million years, according to a new report from University of Arizona geoscientists. The study is the first to explain how the steep-fronted plateau formed.

China's Loess Plateau was formed by wind alternately depositing dust or removing dust over the last 2.6 million years, according to a new report from...

Im Focus: An engineered surface unsticks sticky water droplets

The leaves of the lotus flower, and other natural surfaces that repel water and dirt, have been the model for many types of engineered liquid-repelling surfaces. As slippery as these surfaces are, however, tiny water droplets still stick to them. Now, Penn State researchers have developed nano/micro-textured, highly slippery surfaces able to outperform these naturally inspired coatings, particularly when the water is a vapor or tiny droplets.

Enhancing the mobility of liquid droplets on rough surfaces could improve condensation heat transfer for power-plant heat exchangers, create more efficient...

Im Focus: Increasingly severe disturbances weaken world's temperate forests

Longer, more severe, and hotter droughts and a myriad of other threats, including diseases and more extensive and severe wildfires, are threatening to transform some of the world's temperate forests, a new study published in Science has found. Without informed management, some forests could convert to shrublands or grasslands within the coming decades.

"While we have been trying to manage for resilience of 20th century conditions, we realize now that we must prepare for transformations and attempt to ease...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

Event News

Together - Work - Experience

03.09.2015 | Event News

Networking conference in Heidelberg for outstanding mathematicians and computer scientists

20.08.2015 | Event News

Scientists meet in Münster for the world’s largest Chitin und Chitosan Conference

20.08.2015 | Event News

 
Latest News

Ion implanted, co-annealed, screen-printed 21% efficient n-PERT solar cells with a bifaciality >97%

04.09.2015 | Power and Electrical Engineering

Casting of SiSiC: new perspectives for chemical and plant engineering

04.09.2015 | Machine Engineering

Extremely thin ceramic components made possible by extrusion

04.09.2015 | Materials Sciences

VideoLinks
B2B-VideoLinks
More VideoLinks >>>