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A Survey of ContinuousTime Computation Theory
 Advances in Algorithms, Languages, and Complexity
, 1997
"... Motivated partly by the resurgence of neural computation research, and partly by advances in device technology, there has been a recent increase of interest in analog, continuoustime computation. However, while specialcase algorithms and devices are being developed, relatively little work exists o ..."
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Cited by 29 (6 self)
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Motivated partly by the resurgence of neural computation research, and partly by advances in device technology, there has been a recent increase of interest in analog, continuoustime computation. However, while specialcase algorithms and devices are being developed, relatively little work exists on the general theory of continuoustime models of computation. In this paper, we survey the existing models and results in this area, and point to some of the open research questions. 1 Introduction After a long period of oblivion, interest in analog computation is again on the rise. The immediate cause for this new wave of activity is surely the success of the neural networks "revolution", which has provided hardware designers with several new numerically based, computationally interesting models that are structurally sufficiently simple to be implemented directly in silicon. (For designs and actual implementations of neural models in VLSI, see e.g. [30, 45]). However, the more fundamental...
On the Computational Power of Discrete Hopfield Nets
 In: Proc. 20th International Colloquium on Automata, Languages, and Programming
, 1993
"... . We prove that polynomial size discrete synchronous Hopfield networks with hidden units compute exactly the class of Boolean functions PSPACE/poly, i.e., the same functions as are computed by polynomial spacebounded nonuniform Turing machines. As a corollary to the construction, we observe also th ..."
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Cited by 7 (4 self)
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. We prove that polynomial size discrete synchronous Hopfield networks with hidden units compute exactly the class of Boolean functions PSPACE/poly, i.e., the same functions as are computed by polynomial spacebounded nonuniform Turing machines. As a corollary to the construction, we observe also that networks with polynomially bounded interconnection weights compute exactly the class of functions P/poly. 1 Background Recurrent, or cyclic, neural networks are an intriguing model of massively parallel computation. In the recent surge of research in neural computation, such networks have been considered mostly from the point of view of two types of applications: pattern classification and associative memory (e.g. [16, 18, 21, 24]), and combinatorial optimization (e.g. [1, 7, 20]). Nevertheless, recurrent networks are capable also of more general types of computation, and issues of what exactly such networks can compute, and how they should be programmed, are becoming increasingly topica...
Parallel Programming on Hopfield Nets
"... . We describe a simple general purpose conditionaction type parallel programming language and its implementation on Hopfieldtype neural networks. A prototype compiler performing the translation has been implemented. Keywords: neural networks, Hopfield nets, parallel programming 1 Introduction ..."
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. We describe a simple general purpose conditionaction type parallel programming language and its implementation on Hopfieldtype neural networks. A prototype compiler performing the translation has been implemented. Keywords: neural networks, Hopfield nets, parallel programming 1 Introduction In addition to their uses in specific applications such as pattern classification, associative memory or combinatorial optimization recurrent neural networks are also theoretically universal, i.e., generalpurpose computing devices. It was observed in [7] that sequences of polynomialsize recurrent threshold logic networks are computationally equivalent to (nonuniform) polynomial spacebounded Turing machines, and in [11] this equivalence was extended to polynomialsize sequences of networks with symmetric weights, i.e., "Hopfield nets" with hidden units. A corollary to the latter construction shows also that polynomialsize symmetric networks with small (i.e., polynomially bounded) intercon...
Intrusion Detection and Classification of Attacks in HighLevel Network Protocols Using Recurrent Neural Networks
"... Abstract This paper presents an applicationbased model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocolbased malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore sui ..."
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Abstract This paper presents an applicationbased model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocolbased malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore suitable to work online and for analyzing nonlinear patterns in real time to selfadjust to changes in its input environment. Three different neural networkbased systems have been modelled and simulated for comparison purposes in terms of overall performance: a Feedforward Neural Network, an Elman Network, and a Recurrent Neural Network. Simulation results show that the latter possesses a greater capacity than either of the others for the correct identification and classification of HTTP attacks, and it also reaches a result at a great speed, its somewhat taxing computing requirements notwithstanding. I.