Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. LSTM's and Stacks). However, these aren't really analogous to how a cognitive systems learns how to use it's working memory.

Are there models of how working memory modules (where a saved value decays over time) can be learned to be optimally leveraged? Maybe via reinforcement learning?

Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. LSTM's and Stacks). However, these aren't really analogous to how a cognitive systems learns how to use it's working memory.

Are there models of how working memory modules (where a saved value decays over time) can be learned to be optimally leveraged? Either via reinforcement learning or supervised learning?

Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. LSTM's and Stacks). However, these aren't really analogous to how a cognitive systems learns how to use it's working memory.

Are there models of how working memory can be learned to be optimally leveraged? Maybe via reinforcement learning?

Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. LSTM's and Stacks). However, these aren't really analogous to how a cognitive systems learns how to use it's working memory.

Are there models of how working memory modules (where a saved value decays over time) can be learned to be optimally leveraged? Maybe via reinforcement learning?