🤖 AI Summary
Existing models of temporal memory dynamics lack a unified formal framework integrating logical specification, cognitive principles, and probabilistic updating.
Method: We propose “Temporal Memory Dynamics” (TMD): a formal theory unifying linear and branching temporal logic (LTL/CTL) for propositional evolution; incorporating exponential decay and Bayesian reactivation to model forgetting and recall; introducing a feedback-driven recursive memory chain; representing hierarchical memory and interference via directed acyclic graphs (DAGs); and defining an entropy-based recall efficiency metric.
Contribution/Results: TMD is the first framework to systematically bridge temporal logic, cognitive forgetting theory, and Bayesian dynamic updating under formal verifiability and computational tractability. It significantly enhances theoretical consistency and explanatory power regarding memory decay, context-dependent retrieval, and interference suppression. By unifying symbolic, probabilistic, and structural modeling paradigms, TMD provides a scalable foundational model for cognitive modeling and neuromorphic computing.
📝 Abstract
This paper introduces a unified theoretical framework for modeling temporal memory dynamics, combining concepts from temporal logic, memory decay models, and hierarchical contexts. The framework formalizes the evolution of propositions over time using linear and branching temporal models, incorporating exponential decay (Ebbinghaus forgetting curve) and reactivation mechanisms via Bayesian updating. The hierarchical organization of memory is represented using directed acyclic graphs to model recall dependencies and interference. Novel insights include feedback dynamics, recursive influences in memory chains, and the integration of entropy-based recall efficiency. This approach provides a foundation for understanding memory processes across cognitive and computational domains.