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Memory

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Synchronization of Delayed Memristive Neural Networks: Robust Analysis Approach.

IEEE transactions on cybernetics
This paper considers the asymptotic and finite-time synchronization of drive-response memristive neural networks (MNNs) with time-varying delays. It is known that the parameters of MNNs are state-dependent, and hence the traditional robust control an...

Memory Stacking in Hierarchical Networks.

Neural computation
Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to stud...

Efficient Associative Computation with Discrete Synapses.

Neural computation
Neural associative networks are a promising computational paradigm for both modeling neural circuits of the brain and implementing associative memory and Hebbian cell assemblies in parallel VLSI or nanoscale hardware. Previous work has extensively in...

Associative memory realized by a reconfigurable memristive Hopfield neural network.

Nature communications
Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield...

Dynamic role of adult-born dentate granule cells in memory processing.

Current opinion in neurobiology
Throughout the adult life of all mammals including humans, new neurons are incorporated to the dentate gyrus of the hippocampus. During a critical window that lasts about two weeks, adult-born immature neurons are more excitable and plastic than matu...

Set selection dynamical system neural networks with partial memories, with applications to Sudoku and KenKen puzzles.

Neural networks : the official journal of the International Neural Network Society
After reviewing set selection and memory model dynamical system neural networks, we introduce a neural network model that combines set selection with partial memories (stored memories on subsets of states in the network). We establish that feasible e...

Memory dynamics in attractor networks.

Computational intelligence and neuroscience
As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this...

Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity.

Nano letters
Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dynamic evolutions of internal state variables allow ...

Universal Memcomputing Machines.

IEEE transactions on neural networks and learning systems
We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location. We analyt...

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

International journal of neural systems
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mecha...