Refractory Effects of Chaotic Neurodynamics for Finding Motifs from DNA Sequences

Matsuura, Takafumi; Ikeguchi, Tohru

doi:10.1007/11875581_131

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…On the other hand, some studies applied chaotic dynamics to information retrieval. Matsuura et al showed that the refractory effect of chaotic dynamics is useful for finding the locally similar region (called a motif) in DNA sequence [12]. According to this study, it is important to increase the strength of refractory effects of neurons for searching optimal solutions.…”

Section: Introductionmentioning

confidence: 91%

Effect of refractoriness on learning performance of a pattern sequence

Susumu¹,

Araki²

2009

2009 International Joint Conference on Neural Networks

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The primary purpose of this study is to reveal the effects of refractoriness on learning performance. We simulated that Elman network, which consists of chaotic neurons, learns a pattern sequence using the back-propagation algorithm. Consequently, the learning speed was accelerated about 46% compared with that of the network consisting of integrate-and-fire model neurons. In addition, we analyzed the required number of hidden neurons, asynchronous activities of hidden neurons' refractoriness, and correlation coefficients of synaptic weights after learning. These results suggested that the refractoriness contributes to efficient encoding in the hidden layer of Elman network.

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Section: Introductionmentioning

confidence: 91%

Effect of refractoriness on learning performance of a pattern sequence

Susumu¹,

Araki²

2009

2009 International Joint Conference on Neural Networks

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“…Thus, the optimal solution does not change over time. Many algorithms have been proposed to solve static combinatorial optimization problems using chaotic neurodynamics [19][20][21][22][23][24]. On the contrary, the packet routing problem is a dynamic combinatorial optimization problem that involves the transmission of data packets to their destinations while avoiding packet removal in computer networks.…”

Section: Introductionmentioning

confidence: 99%

An improved routing algorithm using chaotic neurodynamics for packet routing problems

Morita

Kimura

2018

NOLTA

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Abstract:Combinatorial optimization problems consist of static problems such as the traveling salesman problem and the quadratic assignment problem, and dynamic problems such as the packet routing problem and the traffic flow control problem. In static combinatorial optimization problems, the search space for the solution does not change over time and, therefore, neither does the optimal solution. On the contrary, in dynamic combinatorial optimization problems, the search space always changes. Thus, there is no guarantee that the optimal solution for one iteration also applies to the next iteration. In the context of dynamic combinatorial optimization problems, we propose in this paper a heuristic routing method that uses chaotic neurodynamics and degree information to solve the packet routing problem. Numerical experiments showed that the proposed method improves average arrival rate by approximately 130% over the conventional shortest hop method.

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“…As for solving the combinatorial optimization problem, chaotic neurodynamics exhibits higher ability to solve the various combinatorial optimization problems, such as traveling salesman problems (TSP) [20], [21] and quadratic assignment problems (QAP) [22], [23], motif extraction problems (MEP) [24], [25], and vehicle routing problems (VRP) [26], [27]. These strategies use the chaotic dynamics of a chaotic neural network [28] to escape from undesirable local minima.…”

Section: Introductionmentioning

confidence: 99%

Chaotic routing strategy with load-balanced effects for communication networks

Kimura

Ikeguchi

2011

The 2011 International Joint Conference on Neural Networks

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To establish reliable communicate between end users, alleviation of the congestion of packets in the communication networks is the most important problem. Many approaches have been attempting to resolve such a problem. As one of the effective routing strategies for reliable communication, we have also proposed a routing strategy with chaotic neurodynamics. By a refractory effect which is the most important effect of chaotic neuron, the routing strategy shows high performance for communication networks as compared to the shortest path approach. In addition, we improved the routing strategy by combining information of the shortest paths and waiting times at adjacent nodes. As a result, we confirmed that the routing strategy using chaotic neurodynamics is the most effective policy to alleviate the congestion of the packets in the communication network. However, in the previous works, the chaotic routing strategy was evaluated for ideal communication networks; each node has same transmission capability for routing the packets and same size of buffer for storing the packets. From a view point of realistic application of the chaotic routing strategy, it is important to evaluate the performance of the routing strategy under realistic conditions. In 2007, M.Hu et al. proposed a realistic communication network in which the largest storage capacity and processing capability are introduced [5]. Thus, in this paper, we evaluate the chaotic routing strategy for the realistic communication networks [5]. Results show that the chaotic routing strategy keeps the highest arrival rate of the packets as compared to the conventional routing strategies by avoiding the congestion of the packets effectively. Also, we confirmed that the chaotic routing strategy has much possibility for application in the real communication networks.

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Refractory Effects of Chaotic Neurodynamics for Finding Motifs from DNA Sequences

Cited by 12 publications

References 9 publications

Effect of refractoriness on learning performance of a pattern sequence

Effect of refractoriness on learning performance of a pattern sequence

An improved routing algorithm using chaotic neurodynamics for packet routing problems

Chaotic routing strategy with load-balanced effects for communication networks

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