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The combination of experimental neurophysiology with computational studies contributes to the understanding of how brains compute and to the development of neural net technology. Unsupervised receptive field learning can be effectively combined with supervised associative learning, and the way in which synchronization as a signal for grouping is best combined with the processes embodying the criteria specifying what should be grouped.
The goal of this project is to help us understand how the brain works, i.e. how perception, memory, and thought arise from neural activity. We will develop models of neural networks based closely on what is known of the brain, and explore their computational abilities. This is an area of basic science with enormous practical significance. It will guide the design of new massively parallel computers with abilities that are very hard to achieve in conventional computers. It will provide a better understanding of the function of neural structures and processes, and this will in turn will be better understood when analyzed into the elementary steps that neural networks can perform. Progress is currently very rapid in this field, and the USA and Japan are investing strongly in it. Interdisciplinary cooperation is essential. Twinning of the Stirling and Frankfurt laboratories will create an exceptional combination of expertise in computer science, neurobiology, and psychology. It will enable work on projects of fundamental significance that are not possible in either laboratory alone.


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FK9 4LA Stirling
United Kingdom

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Max-Planck-Gesellschaft zur Förderung der Wissenschaften eV
Deutschordenstraße 46
60528 Frankfurt Am Main

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