Introduction

Abstract

Many authors have dwelt on the dispute about the future threats or promises to be expected of Artificial Intelligence: Will AI implementations at some time successfully mimic human intelligence and eventually even replace it? In this book we do not intend to contribute to this debate, not only because we consider it futile. Rather, we try to bring together scientific findings pertinent to an understanding of the mechanisms underlying “intelligent” information processing in brains or machines. Despite the establishment of what is called “cognitive science”, investigations into the field of cognition cannot be restricted to a single discipline. Rather, contributions from various fields of scientific endeavor are required. Results of one discipline will more often than not complement the findings of another in a fruitful dialog. Studies on artificial neural networks (ANNs), for instance, may yield ideas relevant to neural activities in the brain. On the other hand, neurophysiological experiments lead to hypotheses which may be applied to ANNs. Although results obtained with ANNs cannot directly be applied to the case of biological neural nets, they can help to deepen the insight into the performance of brains, to increase the confidence in the plausibility of a hypothesis or to refine it. In this way ANNs have come to be seen as tools in neurophysiological research (cf. the chapters by M. Spitzer, A. K. Engel / P. König, and H. Horner / R. Kühn in this volume).