How Can Machine Intelligence Copy the Brain?
Intelligent machines that not only think for themselves but also actively learn are the vision of researchers of the Institute for Theoretical Science (IGI) at Graz University of Technology...They have been co-ordinating the European Union research project "Brain-i-Nets" (Novel Brain Inspired Learning Paradigms for Large-Scale Neuronal Networks) for three years... _SD
The scientists want to design a new generation of neuro-computers based on the principles of calculation and learning mechanisms found in the brain, and at the same time gain new knowledge about the brain's learning mechanisms.
The human brain consists of a network of several billion nerve cells. These are joined together by independent connections called synapses. Synapses are changing all the time -- something scientists name synaptic plasticity. This highly complex system represents a basis for independent thinking and learning. But even today there are still many open questions for researchers.
"In contrast to today's computers, the brain doesn't carry out a set programme but rather is always adapting functions and reprogramming them anew. Many of these effects have not been explained," comments IGI head Wolfgang Maass together with project co-ordinator Robert Legenstein. In co-operation with neuroscientists and physicists, and with the help of new experimental methods, they want to research the mechanisms of synaptic plasticity in the organism.
...The three-year project is financed by the EU funding framework "Future Emerging Technologies" (FET), which supports especially innovative and visionary approaches in information technology. International experts chose only nine out of the 176 applications, among which was "Brain-i-Nets."
The overall long-term vision of this project is
to develop new design principles for adaptive, reconfigurable very-large-scale hardware systems implementing novel learning rules inspired by biological neural networks in vivo.
Learning mechanisms implemented in the brain appear to be much more robust and flexible than those currently used in neurally inspired computing systems. To confer the superior adaptive and computational capabilities of biological neural systems to large-scale recurrent neural hardware systems and other novel massively parallel computing devices, new and more sophisticated learning rules are needed.
Our long-term vision is that the learning rules for global gating of local learning, identified and explored in this project, will become ideal candidates for implementation in hardware. Conceptually the interaction of local factors that can be monitored and stored at the site of each connection with one or a few global factors is very attractive for hardware implementation. Previous collaborations of several partners of the project have shown that networks of spiking neurons can be implemented in a truly large-scale, parallel, mixed analog-digital hardware system. The inclusion of learning rules that go beyond the classical Hebbian or STDP rules for unsupervised learning, by including a third factor representing for example information on saliency or reward, will advance the hardware into a regime where a much broader class of learning tasks can be solved by these ultra-rapid machines. __Brain-i-Nets
Besides the Graz University of Technology, scientists from University College London, the University of Heidelberg, the University of Zurich, Ecole Polytechnique Federale de Lausanne, and the Centre National de la Recherche Scientifique of France are also partnering in the project.
Al Fin neuroscientists consider this project to be one of the most sophisticated approaches to the biomimetic creation of machine intelligence to this date. For such a project to be truly successful, it is likely to require the expertise of scientists, technologists, and engineers from North America and East Asia, as well as those at the European centres mentioned above.