Neural models for part-whole hierarchies

We present a connectionist method for representing images that ex-plicitly addresses their hierarchical nature. It blends data from neu-roscience about whole-object viewpoint sensitive cells in inferotem-poral cortex8 and attentional basis-field modulation in V43 with ideas about hierarchical descriptions based on microfeatures.5,11 The resulting model makes critical use of bottom-up and top-down pathways for analysis and synthesis.6 We illustrate the model with a simple example of representing information about faces. 1 Hierarchical Models Images of objects constitute an important paradigm case of a representational hi-erarchy, in which 'wholes', such as faces, consist of 'parts', such as eyes, noses and mouths. The representation and manipulation of part-whole hierarchical informa-tion in fixed hardware is a heavy millstone around connectionist necks, and has consequently been the inspiration for many interesting proposals, such as Pollack's RAAM.l1 We turned to the primate visual system for clues. Anterior inferotemporal cortex (IT) appears to construct representations of visually presented objects. Mouths and faces are both objects, and so require fully elaborated representations, presumably at the level of anterior IT, probably using different (or possibly partially overlap-ping) sets of cells. The natural way to represent the part-whole relationship between mouths and faces is to have a neuronal hierarchy, with connections bottom-up from the mouth units to the face units so that information about the mouth can be used to help recognize or analyze the image of a face, and connections top-down from the face units to the mouth units expressing the generative or synthetic knowledge that if there is a face in a scene, then there is (usually) a mouth too. There is littl