AIMC Topic: Memory, Short-Term

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Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning.

Proceedings of the National Academy of Sciences of the United States of America
Human learning embodies a striking duality: Sometimes, we can rapidly infer and compose logical rules, benefiting from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learnin...

Automatic detection of cognitive events using machine learning and understanding models' interpretations of human cognition.

Scientific reports
The pupillary response is a valuable indicator of cognitive workload, capturing fluctuations in attention and arousal governed by the autonomic nervous system. Cognitive events, defined as the initiation of mental processes, are closely linked to cog...

Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.

Scientific reports
Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two d...

Modeling visual working memory using recurrent on-center off-surround neural network with distance dependent inhibition.

Scientific reports
This paper presents a computational model of visual working memory (VWM) that simulates the processing of spatially distributed objects and their features. The model emphasizes the prioritization of object-related information before feature-related p...

Super-resolution of 3D medical images by generative adversarial networks with long and short-term memory and attention.

Scientific reports
Since 3D medical imaging data is a string of sequential images, there is a strong correlation between consecutive images. Deep convolutional networks perform well in extracting spatial features, but are less capable for processing sequence data compa...

Enhanced role of the entorhinal cortex in adapting to increased working memory load.

Nature communications
In daily life, we frequently encounter varying demands on working memory (WM), yet how the brain adapts to high WM load remains unclear. To address this question, we recorded intracranial EEG from hippocampus, entorhinal cortex (EC), and lateral temp...

Shear wave velocity prediction using Long Short-Term Memory Network with generative adversarial mechanism.

PloS one
Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by ...

Causal Discovery Analysis Reveals Insights into Psychosis Proneness, Brain Function, and Environmental Factors among Young Individuals.

Psychiatry research. Neuroimaging
Experiencing mild symptoms of psychosis, like delusions and hallucinations, occurs sometimes in general, nonclinical populations, often termed psychosis proneness (PP), potentially part of the psychosis continuum. Understanding the neural and environ...

Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting.

PloS one
LSTM (Long Short-Term Memory Network) is currently extensively utilized for forecasting financial time series, primarily due to its distinct advantages in separating the long-term from the short-term memory information within a sequence. However, the...

Use of posterior probabilities from a long short-term memory network for characterizing dance behavior with multiple accelerometers.

Journal of Alzheimer's disease : JAD
BackgroundDancing may be protective for cognitive health among adults with mild cognitive impairment, Alzheimer's disease or dementia; however, additional methods are needed to characterize motor behavior quality in studies of dance.ObjectiveTo deter...