Neural Models for Part-Whole Hierarchies

We present a connectionist method for representing images that explicitly addresses their hierarchical nature. It blends data from neuroscience about whole-object viewpoint sensitive cells in inferotemporal cortex and attentional basis-field modulation in V4 with ideas about hierarchical descriptions based on microfeatures. The resulting model makes critical use of bottom-up and top-down pathways for analysis and synthesis. We illustrate the model with a simple example of representing information about faces.

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