Context discerning multifunction networks: reformulating fixed weight neural networks

Research in recurrent neural networks has produced a genre of networks referred to as fixed weight neural networks (FWNNs) which have the ability to adapt without changing explicit weights. FWNNs are unique in that they adapt their processing based on the spatiotemporal characteristics of the incoming signal without need for weight change. As a result, a single FWNN is able to model and control many families of disparate systems without weight changes. FWNNs pose an interesting model for contextual memory in neural systems. The work reported takes a FWNN, decomposes it and analyzes its internal workings. Using new insight, FWNNs are reformulated into a simpler structure, context discerning multifunction networks (CDMN).