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Memory

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Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

Computational intelligence and neuroscience
We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features fro...

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

PloS one
From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-s...

Artificial neural networks reveal individual differences in metacognitive monitoring of memory.

PloS one
Previous work supports an age-specific impairment for recognition memory of pairs of words and other stimuli. The present study tested the generalization of an associative deficit across word, name, and nonword stimulus types in younger and older adu...

Artificial Sensory Memory.

Advanced materials (Deerfield Beach, Fla.)
Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intellige...

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from as...

A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus.

Nature neuroscience
The hippocampus is able to rapidly learn incoming information, even if that information is only observed once. Furthermore, this information can be replayed in a compressed format in either forward or reverse modes during sharp wave-ripples (SPW-Rs)....

Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks.

Neural networks : the official journal of the International Neural Network Society
The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for...

Defining Image Memorability Using the Visual Memory Schema.

IEEE transactions on pattern analysis and machine intelligence
Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The...

A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices.

Computational intelligence and neuroscience
Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and ...

Influence of New Technologies on Post-Stroke Rehabilitation: A Comparison of Armeo Spring to the Kinect System.

Medicina (Kaunas, Lithuania)
BACKGROUND: New technologies to improve post-stroke rehabilitation outcomes are of great interest and have a positive impact on functional, motor, and cognitive recovery. Identifying the most effective rehabilitation intervention is a recognized prio...