Development of perceptual context-sensitivity in unsupervised neural networks: parsing, grouping, and segmentation

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner is described. Exposure to a perceptual environment during a developmental period configures the network to perform appropriate organization of sensory data. An anti-Hebbian learning rule causes some lateral inhibitory connection of sensory data. An anti-Hebbian learning rule causes some lateral inhibitory connection weights to weaken, thereby letting multiple neurons become simultaneously active. The rule lets other inhibitory weights remain strong; these enforce specific simultaneous contextual consistency constraints on allowable combinations of activations. EXIN networks perform near-optimal parallel parsing of multiple superimposed patterns, by simultaneous distributed activation multiple neurons. EXIN networks implement a form of credit assignment