Modeling neural network semantics

Summary form only given, as follows. Simplifying assumptions allow identification of the logical equivalent of the computations in a class of neural networks. Higher-order functions encapsulate the semantic content of patterns of signals and synaptic weights by forming formulas in a formal logic. This model was applied to capture the semantic content of a simple, hierarchical network that learns to identify the cardinalities of subsets of a finite set.< >