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Short term memory and the EEG [PDF]

Nature, 1974
STUDIES of human short term memory and physiological state have produced what seem to be paradoxical results, as efficient performance has been associated with low states of activation. This effect was first demonstrated by Kleinsmith and Kaplan1 who measured electrodermal response (EDA) to paired associates.
Adrian Smallbone, Dylan Marc Jones, Anthony Gale   +2 more
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Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Interspeech, 2014
Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs.
Hasim Sak, A. Senior, F. Beaufays
semanticscholar   +1 more source

Working memory, short-term memory, and general fluid intelligence: a latent-variable approach.

Journal of experimental psychology. General, 1999
A study was conducted in which 133 participants performed 11 memory tasks (some thought to reflect working memory and some thought to reflect short-term memory), 2 tests of general fluid intelligence, and the Verbal and Quantitative Scholastic Aptitude ...
R. Engle, Stephen W. Tuholski, J. Laughlin, Andrew R. A. Conway   +3 more
semanticscholar   +1 more source

A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware

Nature Machine Intelligence, 2021
Spike-based neuromorphic hardware holds promise for more energy-efficient implementations of deep neural networks (DNNs) than standard hardware such as GPUs.
Philipp Plank, A. Rao, Andreas Wild, W. Maass   +3 more
semanticscholar   +1 more source

Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

IEEE Transactions on Industrial Informatics, 2020
Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly.
Xiaofeng Yuan, Lin Li, Yalin Wang
semanticscholar   +1 more source

Video Summarization with Long Short-Term Memory

European Conference on Computer Vision, 2016
We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM),
Ke Zhang, Wei-Lun Chao, Fei Sha, K. Grauman   +3 more
semanticscholar   +1 more source

Short-Term Memory

2013
According to the Atkinson and Shiffrin model, information is held in STM by continually rehearsing it, and without rehearsal it will be almost immediately forgotten. The LTM is seen as a more passive store of information which is available for retrieval but not kept in an activated form.
Laudan B. Jahromi, Crystal I. Bryce
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Short-term memory for symmetry

Vision Research, 1976
Abstract Symmetric cascades of dots were generated in a continuous random sequence such that each dot had a partner reflected about a vertical or horizontal axis, respectively. Between each point and its partner a temporal delay was introduced. While the brightness of the dots appeared constant within 120–140 msec, symmetry perception ceased at ...
John Hogben, John Ross, Bela Julesz
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Short-Term Memory for Numbers

Human Factors: The Journal of the Human Factors and Ergonomics Society, 1990
Each of 52 (25 female and 27 male) high school students reproduced from memory 1000 eight-digit numbers after viewing each number for 5 s. Subjects were given unlimited time to reproduce the numbers and were allowed to change their reproductions. The range of errors was very large: from 71 to 2231 out of 8000 digits reproduced by each subject.
John V. Moulden, Alphonse Chapanis
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Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

IEEE Transactions on Vehicular Technology, 2018
Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning
Yongzhi Zhang, R. Xiong, Hongwen He, M. Pecht   +3 more
semanticscholar   +1 more source