scholarly journals A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data

2020 ◽  
pp. 479-488
Author(s):  
Chongyue Zhao ◽  
Hongming Li ◽  
Zhicheng Jiao ◽  
Tianming Du ◽  
Yong Fan
Keyword(s):  
Short Term Memory ◽  
Fmri Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI

2021 ◽  
Author(s):  
Anant Mittal ◽  
Priya Aggarwal ◽  
Luiz Pessoa ◽  
Anubha Gupta
Keyword(s):  
Short Term Memory ◽  
Fmri Data ◽  
Brain State ◽  
Short Term ◽  
Term Memory ◽  
Feature Representations ◽  
Human Connectome Project ◽  
Brain States ◽  
Long Short Term Memory ◽  
The Brain

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information avail- able in the fMRI data. Long short term memory (LSTM), a class of machine learning model possessing a "memory" component, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bidirectional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18 percent better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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