Models for Evaluating Sequentail Memories in a Recurrent Neuronal Network

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The storage of sequences of behavioral events is a fundamental strategy of memory organization. The computational algorithms underlying the storage and retention of sequential memories on the neuronal level are not well understood. Sequential memory can be realized through a recurrent network as it is found in the CA3 region of the hippocampus, a brain structure critically involved in the online acquisition of episodic memories. The recurrent synaptic connections that are thought to store sequential memories in CA3 are highly plastic and particularly sensitive to neuromodulators. Rewiring of CA3 pyramidal neurons, and hence changes of the memory of behavioral sequences, therefore depends on the behavioral evaluation in down-stream regions. At first, we intend to theoretically assess retention times in a recurrent neuronal network model for a situation in which different memory traces are assigned different behavioral importance (value). In a second step, we develop a biologically plausible theoretical model for value-dependent modification of existing sequential memories. Third, we address the problem that behavioral sequences require associations over time intervals of arbitrary duration by investigating how to store temporal hierarchies of sequences.