Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks

Relationships between locally interconnected neural networks that use receptive field representations and trellis or convolutional codes are explored. A fault tolerant neural network is described. It is patterned after the trellis graph description of convolutional codes and is able to tolerate errors in its inputs and failures of constituent neurons. This network incorporates learning, which adds failure tolerance; the network is able to modify its connection weights an internal representation so that spare neurons can replace neurons which fail. A brief review of trellis-coding concepts is included.