A Cortical-type Modular Neural Network for Hypothetical Reasoning

We propose a multilayer neural network architecture that can implement the kind of hypothetical reasoning that the cortex seems to perform in making sense of the sensory input. The elementary processing nodes of each homogeneous sheet are not single formal neurons, but complex modules abducted from the functional organization of neocortical columns. As an example, we simulate face recognition in this neocortical architecture. A holistic but coarse initial hypothesis is generated by express forward input description and subsequently refined under the constraints of this hypothesis. Separation of forward input description and feedback generated hypothesis, while using the difference in both descriptions at each of the modular units to control the refinement, enables robust recognition and has the potential for autonomous learning. Copyright 1997 Elsevier Science Ltd.