Generalization Grandmother Model for Vision Neural Network Assembly Memory Model One-Step Learning Learning from one Example Neuron Receptive Field Bell-Shaped Tuning Semi-Representation
Institute of Information Theories and Applications FOI ITHEA
Usually, generalization is considered as a function of learning from a set of examples. In present work
on the basis of recent neural network assembly memory model (NNAMM), a biologically plausible 'grandmother'
model for vision, where each separate memory unit itself can generalize, has been proposed. For such a
generalization by computation through memory, analytical formulae and numerical procedure are found to
calculate exactly the perfectly learned memory unit's generalization ability. The model's memory has complex
hierarchical structure, can be learned from one example by a one-step process, and may be considered as a
semi-representational one. A simple binary neural network for bell-shaped tuning is described.