MIND: Memory Integration as Nonlinear Dynamics Modeling Memory as Multi-Vector Attractor Dynamics

This paper proposes a formal theoretical framework that reconceptualizes human memory as a nonlinear, multi-vector dynamical system, challenging traditional computational models based on linear algebraic representations. Rather than treating memory as a passive storage-and-retrieval mechanism, we model it as an active reconstruction process emerging from interactions among distributed neural subsystems. Drawing from dynamical systems theory, differential topology, and computational neuroscience, we formalize memory recall as a nonlinear traversal through a multidimensional vector field shaped by emotional, sensory, and contextual influences. We introduce a set of governing equations that describe how these subsystems contribute dimension-specific information to generate coherent conscious experiences. The model accounts for emotional weighting, reconsolidation drift, and state-dependent variability in recall, offering testable predictions for neuroimaging and psychological research. It also suggests new directions for trauma-informed therapeutic interventions and provides design principles for building more neuromorphic, emotionally aware AI systems. This work represents a novel synthesis that bridges neurobiological memory mechanisms with their phenomenological and computational manifestations.