Introduction

It is an assumption of the neural computation community that the brain as the most successful pattern recognition system is a useful model for deriving efficient algorithms on computers. But how can a useful interaction between brain research and artificial object recognition be realized? We see two questionable ways of interaction. On the one hand, a very detailed modelling of biological networks may lead to a disregard of the task solved in the brain area being modelled. On the other hand, the neural network community may lose credibility by a very rough simplification of functional entities of brain processing. This may result in a questionable naming of simple functional entities as neurons or layers to pretend biological plausibility. In our view, it is important to understand the brain as a tool solving a certain task and therefore it is important to understand the functional meaning of principles of cortical processing, such as hierarchical processing, sparse coding, and ordered arrangement of features. Some researchers (e.g., Barlow, 1961c; Palm, 1980; Földiák, 1990; Atick, 1992b; Olshausen and Field, 1997) have already made important steps in this direction. They have given an interpretation of some of the above-mentioned principles in terms of information theory. Others (e.g. Hummel and Biederman, 1992; Lades et al., 1992) have tried to initiate an interaction between brain research and artificial object recognition by building efficient and biologically motivated object recognition systems. Following these two lines of research, we suggest to look at a functional level of biological processing and to utilize abstract principles of cortical processing in an artificial object recognition system.