From categorical semantics to neural network design

We introduce a new architecture designed by applying a recently-developed mathematical model of neural network semantics using category theory. The new design has multiple subnetworks associated with different sensors and association regions. The subnetworks form individual, hierarchical representations of a body of knowledge. Subnetwork interconnections adapt to link the individual concept representations appropriately and provide knowledge coherence, representing a single knowledge heirarchy across the multi-sensor network.