Simple Synchrony Networks : Learning to Parse Natural Language with Temporal Synchrony Variable Binding

The Simple Synchrony Network (SSN) is a new connectionist architecture, incorporating the insights of Temporal Synchrony Variable Binding (TSVB) into Simple Recurrent Networks. The use of TSVB means SSNs can output representations of structures, and can learn generalisations over the constituents of these structures (as required by systematicity). This paper describes the SSN and an associated training algorithm, and demonstrates SSNs’ generalisation abilities through results from training SSNs to parse real natural language sentences.