Novel lists of 7 +/- 2 known items can be reliably stored in an oscillatory short-term memory network: interaction with long-term memory.

Novel lists of 7 +/- 2 known items can be reliably stored in an oscillatory short-term memory network: interaction with long-term memory.

This paper proposes a model for the short-term memory (STM) of unique lists of known items, as, for instance, a phone number. We show that the ability to accurately store such lists in STM depends strongly on interaction with the preexisting long-term memory (LTM) for individual items (e.g., digits). We have examined this interaction in computer simulations of a network based on physiologically...

the effects of keyword and context methods on pronunciation and receptive/ productive vocabulary of low-intermediate iranian efl learners: short-term and long-term memory in focus

از گذشته تا کنون، تحقیقات بسیاری صورت گرفته است که همگی به گونه ای بر مثمر ثمر بودن استفاده از استراتژی های یادگیری لغت در یک زبان بیگانه اذعان داشته اند. این تحقیق به بررسی تاثیر دو روش مختلف آموزش واژگان انگلیسی (کلیدی و بافتی) بر تلفظ و دانش لغوی فراگیران ایرانی زیر متوسط زبان انگلیسی و بر ماندگاری آن در حافظه می پردازد. به این منظور، تعداد شصت نفر از زبان آموزان ایرانی هشت تا چهارده ساله با...

Short-Long-Term Memory Interaction with Underlearned Long-Term Storage.

An Oscillatory Hebbian Network Model of Short-Term Memory

Recurrent neural architectures having oscillatory dynamics use rhythmic network activity to represent patterns stored in short-term memory. Multiple stored patterns can be retained in memory over the same neural substrate because the network's state persistently switches between them. Here we present a simple oscillatory memory that extends the dynamic threshold approach of Horn and Usher (1991...

Long Short-term Memory

Model compression is significant for the wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and business clusters requiring quick responses to large-scale service requests. This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden s...