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. First, we consider some simple memory binding tasks in which the output is set to be a copy of the given input and a selective sequence of previous inputs online. Next, we design a more complex binding taskbased on a cue that encodes binding relations . 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  . 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.
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.
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First, we consider some simple memory binding tasks in which the output is set to be a copy of the given input and a selective sequence of previous inputs online. Next, we design a more complex binding taskbased on a cue that encodes binding relations
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
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. 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