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Memory, Short-Term

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Recurrent Neural Networks With External Addressable Long-Term and Working Memory for Learning Long-Term Dependences.

IEEE transactions on neural networks and learning systems
Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs called an external addressable long-term and work...

Emotional arousal amplifies competitions across goal-relevant representation: A neurocomputational framework.

Cognition
Emotional arousal often facilitates memory for some aspects of an event while impairing memory for other aspects of the same event. Across three experiments, we found that emotional arousal amplifies competition among goal-relevant representations, s...

Task representations in neural networks trained to perform many cognitive tasks.

Nature neuroscience
The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cogniti...

Brain dynamics and temporal trajectories during task and naturalistic processing.

NeuroImage
Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. ...

Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning.

Biological psychology
OBJECTIVE: According to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also ...

A self-organizing short-term dynamical memory network.

Neural networks : the official journal of the International Neural Network Society
Working memory requires information about external stimuli to be represented in the brain even after those stimuli go away. This information is encoded in the activities of neurons, and neural activities change over timescales of tens of milliseconds...

A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks.

Neural computation
To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent we...

Visual mental imagery: A view from artificial intelligence.

Cortex; a journal devoted to the study of the nervous system and behavior
This article investigates whether, and how, an artificial intelligence (AI) system can be said to use visual, imagery-based representations in a way that is analogous to the use of visual mental imagery by people. In particular, this article aims to ...

Cortical computations via transient attractors.

PloS one
The ability of sensory networks to transiently store information on the scale of seconds can confer many advantages in processing time-varying stimuli. How a network could store information on such intermediate time scales, between typical neurophysi...

Working Memory and Decision-Making in a Frontoparietal Circuit Model.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and dis...