  Working Memory is the brain module that holds and manipulates information
online. In this work, we design a hybrid model in which a simple feed-forward
network is coupled to a balanced random network via a read-write vector called
the interface vector. Three cases and their results are discussed similar to
the n-back task called, first-order memory binding task, generalized
first-order memory task, and second-order memory binding task. The important
result is that our dual-component model of working memory shows good
performance with learning restricted to the feed-forward component only. Here
we take advantage of the random network property without learning. Finally, a
more complex memory binding task called, a cue-based memory binding task, is
introduced in which a cue is given as input representing a binding relation
that prompts the network to choose the useful chunk of memory. To our
knowledge, this is the first time that random networks as a flexible memory is
shown to play an important role in online binding tasks. We may interpret our
results as a candidate model of working memory in which the feed-forward
network learns to interact with the temporary storage random network as an
attentional-controlling executive system.
