407 research outputs found
On the validity of memristor modeling in the neural network literature
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 of memristive elements: they describe either
non-linear resistors or certain bi-state systems, which all are devices without
memory. Therefore, the results presented in a significant number of
publications are at least questionable, if not completely irrelevant to the
actual field of memristive neural networks
Dynamical Analysis for High-Order Delayed Hopfield Neural Networks with Impulses
The global exponential stability and uniform stability of the equilibrium point for high-order delayed Hopfield neural networks with impulses are studied. By utilizing Lyapunov functional method, the quality of negative definite matrix, and the linear matrix inequality approach, some new stability criteria for such system are derived. The results are related to the size of delays and impulses. Two examples are also given to illustrate the effectiveness of our results
Recent Advances and Applications of Fractional-Order Neural Networks
This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
Recent advances in Voice Activity Detection (VAD) are driven by artificial
and Recurrent Neural Networks (RNNs), however, using a VAD system in
battery-operated devices requires further power efficiency. This can be
achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs)
to perform inference at very low energy consumption. Spiking networks are
characterized by their ability to process information efficiently, in a sparse
cascade of binary events in time called spikes. However, a big performance gap
separates artificial from spiking networks, mostly due to a lack of powerful
SNN training algorithms. To overcome this problem we exploit an SNN model that
can be recast into an RNN-like model and trained with known deep learning
techniques. We describe an SNN training procedure that achieves low spiking
activity and pruning algorithms to remove 85% of the network connections with
no performance loss. The model achieves state-of-the-art performance with a
fraction of power consumption comparing to other methods.Comment: 5 pages, 2 figures, 2 table
New Results for Periodic Solution of High-Order BAM Neural Networks with Continuously Distributed Delays and Impulses
By M-matrix theory, inequality techniques, and Lyapunov functional method, certain sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential stability of periodic solution for a new type of high-order BAM neural networks with continuously distributed delays and impulses. These novel conditions extend and improve some previously known results in the literature. Finally, an illustrative example and its numerical simulation are given to show the feasibility and correctness of the derived criteria
Stability analysis of impulsive stochastic CohenāGrossberg neural networks with mixed time delays
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic CohenāGrossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic CohenāGrossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany
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