AIMC Topic: Memory

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On the validity of memristor modeling in the neural network literature.

Neural networks : the official journal of the International Neural Network Society Sep 9, 2019
An analysis of the literature shows that there are two types of non-memristive models that have been widely used in the modeling of so-called "memristive" neural networks. Here, we demonstrate that such models have nothing in common with the concept ...
Neural Networks, Computer Reproducibility of Results Memory
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Transformed ℓ regularization for learning sparse deep neural networks.

Neural networks : the official journal of the International Neural Network Society Aug 27, 2019
Deep Neural Networks (DNNs) have achieved extraordinary success in numerous areas. However, DNNs often carry a large number of weight parameters, leading to the challenge of heavy memory and computation costs. Overfitting is another challenge for DNN...
Neural Networks, Computer Machine Learning Neurons Memory Algorithms
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Robust computation with rhythmic spike patterns.

Proceedings of the National Academy of Sciences of the United States of America Aug 20, 2019
Information coding by precise timing of spikes can be faster and more energy efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limited their use in theoretical neuroscience and computing applications. He...
Models, Neurological Humans Memory Neural Networks, Computer Action Potentials Interneurons Nerve Net
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Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

Computational intelligence and neuroscience Aug 1, 2019
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...
Attention Memory Algorithms Data Collection Language Humans Deep Learning Neural Networks, Computer
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Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

PloS one Aug 1, 2019
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...
Bayes Theorem Humans Models, Neurological Memory Association Learning Mental Recall Neural Networks, Computer Action Potentials
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Artificial neural networks reveal individual differences in metacognitive monitoring of memory.

PloS one Jul 31, 2019
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...
Age Factors Memory Disorders Memory Recognition, Psychology Individuality Adult Middle Aged Adolescent Aged, 80 and over Young Adult Association Learning Neural Networks, Computer Humans Metacognition Female Aged
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Artificial Sensory Memory.

Advanced materials (Deerfield Beach, Fla.) Jul 30, 2019
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...
Neurons Semiconductors Artificial Organs Artificial Intelligence Robotics Synapses Memory
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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 Jul 3, 2019
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...
Neural Networks, Computer Models, Neurological Action Potentials Memory Conditioning, Classical Neurons Association Learning Synapses
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A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus.

Nature neuroscience Jun 24, 2019
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)....
Neuronal Plasticity Memory Interneurons Septal Nuclei CA3 Region, Hippocampal Computer Simulation Learning Time Factors Neural Networks, Computer
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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 Jun 18, 2019
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...
Memory Computer Simulation Learning Neuronal Plasticity Humans Neural Networks, Computer Time Factors Synapses Brain
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