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This paper argues for the development of synthetic approaches towards the study of brain and behavior as a complement to the more traditional empirical mode of research. As an example we present our own work on learning and problem solving which relates to the behavioral paradigms of classical and operant conditioning. We define the concept of learning in the context of behavior and lay out the basic methodological requirements a model needs to satisfy, which includes evaluations using robots. In addition, we define a number of design principles neuronal models should obey to be considered relevant. We present in detail the construction of a neural model of short- and long-term memory which can be applied to an artificial behaving system. The presented model (DAC4) provides a novel self-consistent implementation of these processes, which satisfies our principles. This model will be interpreted towards the present understanding of the neuronal substrate of memory.

What Can Robots Tell Us About Brains? A Synthetic Approach Towards the Study of Learning and Problem Solving