From neural networks to neural strategies

Artificial neural networks have evolved from their biologically inspired roots to a well established means to solve a broad spectrum of engineering problems. Their embedding into modern statistics has provided the necessary theoretical foundation for challenging engineering tasks, such as advanced real time image and signal processing. These are exemplary demonstrations for the applicability of this approach to complex information processing. However, the large number of applications must not obscure the fact that there are some major unsolved problems concerning neural networks. There are still no satisfactorily constructive ways to determine the optimal structure (elements as well as organization) or the learning and evaluation dynamics. Ongoing research addresses these problems. In addition to pursuing this direction, one can ask what other lessons we can learn from biology concerning complex information processing. Our goal is to sketch a possible pathway from neural networks to more comprehensive neural strategies.

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