VII. CONCLUSION
This paper proposed an integrated semantic and EM model. Integration improves recognition in EM by providing associated context that helps to trigger EM traces. EM influ ences activation of the SM neurons, removing ambiguities of sequential recall within a specific context. This is important in situations where the exact recall of an event is needed, rather than recall of all associated events.
An integrated declarative memory can efficiently store, consolidate, and retrieve information while considering event significance. It uses a forgetting mechanism to remove unimportant events. The model satisfies the functional requirements needed in cognitive systems and may be used to control autonomous robots using motivated reinforcement learning [18]. It is needed for symbol grounding, object recognition, concept development, cognitive understanding of perceptions, feelings, and emotions in motivated agents.
Our future work is to integrate this model with other functional blocks of the MLECOG architecture to make full use of the SM in machine learning, planning, anticipation, and thinking.
