The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and Integration

Contemporary neuroscience has generated extensive empirical insights into perception, memory, prediction, valuation, and consciousness. However, it still lacks an explicit operational architecture capable of explaining how these processes emerge from a unified computational mechanism. This work introduces DIME (Detect–Integrate–Mark–Execute), a unified operational architecture in which perception, memory, valuation, and conscious access are treated as components of a single recurrent computational cycle. The framework is organized around four core elements: engrams, defined as distributed recurrent neural structures that support multiple activation trajectories rather than static memory traces; execution threads, representing temporally extended, causally coherent trajectories of neural activity; marker systems, corresponding to neuromodulatory and limbic mechanisms that regulate value, selection, plasticity, and trajectory competition; and hyperengrams, large-scale integrative states associated with global coordination and conscious access. Within this formulation, DIME provides a mapping between local neural assemblies, temporal sequence dynamics, value-based modulation, and large-scale network integration. Rather than treating perception, memory, and decision-making as partially independent processes, the framework interprets them as different expressions of a single operational loop acting across multiple spatial and temporal scales. The proposed architecture is consistent with empirical findings on hippocampal indexing, recurrent cortical processing, neuromodulatory control, and large-scale network dynamics, while remaining sufficiently general to support applications in artificial intelligence and robotics. Unlike frameworks centered on prediction, memory storage, or global broadcasting, DIME proposes that cognition arises from the recurrent interaction between executable representational structures, trajectory-based processing, value-guided selection, and dynamic large-scale integration. The framework generates explicit and falsifiable predictions regarding context-dependent neural trajectories, marker-mediated state transitions, and large-scale network reconfiguration. In this sense, DIME is not intended as a metaphorical synthesis, but as a testable architectural hypothesis for neuroscience and biologically inspired cognitive systems. Beyond theoretical neuroscience, the framework is also positioned as a transferable design-level reference model for adaptive AI systems, autonomous robotics, and cognitively informed engineering architectures operating in dynamic environments.