Do orthographic LTM representations influence verbal short-term memory?

memory and comprehension

memory and pheme awareness 

memory and visual attention 

memory and rapid automatized naming

As far as I've seen, MANN architectures employ external memories in formats other than neural networks. Some of the advantages in these architectures stem exactly from this hybridization, compensating for NN drawbacks (eg. 'catastrophic forgeting'). On the other hand, multi-NNs models (eg. Generative Adversarial Networks, GANs) also appear very promising. I am curious whether there are models out there which use NNs, not only for processing, but for memory too?

Working memory, a commonly affected memory process, is a crucial bridge between short term & long term memory processes. It influences the efficiency of the task being performed, and also our learning from past memories. Yet it is 'short-lived' and difficult to evaluate, especially in human beings.

8 month olds were first familiarized with a language and following a 10-min delay tested on target words that were in the language. According to Horst & Samuelson (2008), even 5 min delay taps into long-term memory. But I am a little confused about terminology used in memory literature. 

Does anyone have any experience with conducting the novel object recognition test while administering a drug that has a short duration of action (so that it may "wear off" during the seemed-to-be "obligatory" 1 hour of retention time between the "training" session [T1] and the "test" session [T2])? Should it be administered nevertheless, before the T1? Or maybe after the T1? Or in any other way?

Time series reveal hidden behaviour in the form of low frequency cycles that is named long term memory or persistence. Short term memory is usually described with the autocorrelation function and it is possible to model stochastic processes using, for example, a Cholevsky decomposition, nevertheless I did not see any work preserving the long term memory, yet. A property of persistance is that it remains the same when using diferrent time resolutions for the studied process. A classical example are discharge flow time series in rivers, rainfall, solar activity index etc. To see the hidden cycles of persistance a transformation of data is performed, please see the attached file. Attached you can see a transformation of the Magdelena river discharges. The transformation performs as follows:

Let's say Qi are Magdalena river discharges at time "i" then ki=Qi/Qo where Qo is the Q expected value. Let Cv be the coefficient of variation for Q time series then we define CDIi = sum((ki-1)/Cv) as de cummulative sum of standarized ki.

In the attachment the red line and the blue one are the same charts but using Qi at a different time resolution (red is monthly data, blue is daily).

Instruction is a systematic process in which every component (i.e. teachers, students, materials, and learning environment) is crucial to successfully learning (Dick & Carey, 1996). Instruction deals with teaching and learning activities. These activities should assist students to learn knowledge and move this knowledge from short term memory to long term memory. To do that, students need to learn how to rehearse, encode, process and feedback new knowledge to be able to remember when they need.

FMC-9: How does the long-term memory (LTM) in visual cortex be retrieved in order to rebuild the image in short-term memory (STM) for perceptions and reasoning?