Pattern recognition via synchronization in phase-locked loop neural networks

Hoppensteadt, Frank C.; Izhikevich, Eugene M.

doi:10.1109/72.846744

Cited by 360 publications

(191 citation statements)

References 15 publications

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“…Many NNs rely on a synchronous behavior for a proper functioning, e.g., information transmission, pattern recognition, and learning [29], [30]. Hence, this brief will be devoted to studying the synchronization of coupled NNs.…”

Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances

Cao

et al. 2011

IEEE Trans. Neural Netw.

354

155

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of uncertain Hopfield neural networks with discrete and distributed delays," Phys. Lett. A, vol. 354, no. 4, pp. 288-297, Jun. 2006. [43] Z. Wang, Y. Liu, L. Yu, and X. Liu, "Exponential stability of delayed recurrent neural networks with Markovian jumping parameters," Phys. Lett. A, vol. 356, nos. 4-5, pp. 346-352, Aug. 2006. condition is closely related with the time delay, impulse strengths, average impulsive interval, and coupling structure of the systems. The obtained criterion is given in terms of an algebraic inequality which is easy to be verified, and hence our result is valid for largescale systems. The results extend and improve upon earlier work. As a numerical example, a small-world network composing of impulsive coupled chaotic delayed NN nodes is given to illustrate our theoretical result. Exponential Synchronization of Linearly Coupled Neural Networks with Impulsive Disturbances

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Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances

Cao

et al. 2011

IEEE Trans. Neural Netw.

354

155

View full text Add to dashboard Cite

show abstract

“…To illustrate how to implement an Oscillatory Neural Network in hardware, we choose the example of an oscillator network composed of phase-locked loops [17]. The behavior and structure of this simple network helps to understand how Oscillatory Neural Networks can store and retrieve patterns.…”

Section: Implementation Of Oscillator Networkmentioning

confidence: 99%

Alternative Architectures for NonBoolean Information Processing Systems

Fang¹,

Levitan²,

Chiarulli³

et al. 2014

Emerging Nanoelectronic Devices

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In this chapter we explore algorithms and develop an architecture based on emerging nanodevice technologies for cognitive computing tasks such as recognition, classification, and vision. Though the performance of computing systems has been continuously improving for decades, for cognitive tasks that humans are adept at, our machines are usually ineffective despite the rapid increases in computing speed and memory size. Moreover, given the fact that CMOS technology is approaching the limits of scaling, computing systems based on traditional Boolean logic may not be the best choice for these special computing tasks due to their massive parallelism and complex nonlinearities. On the other hand, recent research progress in nanotechnology provides us opportunities to access alternative devices with special nonlinear response characteristics that fit cognitive tasks better than general purpose computation. Building circuits, architectures, and algorithms around these emerging devices may lead us to a more efficient solution to cognitive computing tasks or even artificial intelligence. This chapter presents several nonBoolean architecture models and algorithms based on nano-oscillators to address image pattern recognition problems.The idea of special hardware architectures for cognitive tasks is not new. Equivalent circuits for a neuron model go back at least to 1907, where the model was just simply composed of a resistor and a capacitor [1]. Since then, various models have been implemented based on different technologies. In the late 1980s, Carver Mead proposed the term neuromorphic system in reference to all the electronic implementations of neural systems [2]. Meanwhile, with the development of digital computing systems, theoretical and computational models for neural

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“…Hoppensteadt and Izhikevich [15] illustrate their method with a single example using three inputs and have not applied their methodology to a larger number of inputs or test cases, or addressed the segmentation problem. Furthermore, they raise the issue that the Hebbian learning rule they use may not be the best.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Segmentation With Dynamical Units

Rao

Cecchi

Peck

et al. 2008

IEEE Trans. Neural Netw.

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Abstract-In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that separation is determined by the amplitude of units in an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on a Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs. The network dynamics are derived from an objective function that rewards sparse coding in the generalized amplitude-phase variables. We argue that this objective function can provide a possible formal interpretation of the binding problem and that the implementation of the network architecture and dynamics is biologically plausible.

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Pattern recognition via synchronization in phase-locked loop neural networks

Cited by 360 publications

References 15 publications

Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances

Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances

Alternative Architectures for NonBoolean Information Processing Systems

Unsupervised Segmentation With Dynamical Units

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