A Connectionist Cognitive Model for Temporal Synchronisation and Learning

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Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, background): “…of Connectionist Modal Logics (CML) and Connectionist Temporal Logics of Knowledge (CTLK) [8, 9], researchers were able to learn full solutions to several problems in distributed, multiagent learning and reasoning, including the Muddy Children Puzzle [8] and the Dining Philosophers Problem [26].”
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017, methodology): “The cognitive temporal logic described in (Lamb et al., 2007) is used to represent domain knowledge in terms of beliefs and previously-applied rules.”
Learning and Representing Temporal Knowledge in Recurrent Networks (2011, unknown): “poral logic used is an extension of the logic used in [14] with a richer language containing both future and past operators.”