The basic idea of our anticipatory approach to perception is to avoid the common separation of perception and generation of behavior and to fuse both aspects into a consistent neural process. Our approach is based on the prediction of theconsequences of hypothetically executed actions. In this sense, perception of space and shape is assumed to be a generative process of anticipating the course of events resulting from different sequences of actions. We present a biologically motivatedcomputational model that is able to anticipate and evaluate hypothetical sensorimotor sequences. Our Model for Anticipation based on Cortical Representations (MACOR) allows a completely parallel search at the neocortical level using assemblies of ratecoded neurons for grouping, separation, and selection of sensorimotor sequences.
展开▼
