Deep dynamic spiking neural P systems with applications in organ segmentation

Qiu, Chenggong; Xue, Jie; Liu, Xiyu; Li, Qi

doi:10.1007/s41965-022-00115-4

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…Membrane computing has been intensively studied. Some of the most recent papers refer to theoretical developments [6][7][8][9][10][11] and applications [12][13][14][15][16]. The possibility of generating exponential space in polynomial (more often linear) time, is a key feature of these models and represents the main element in solving NP-complete problems [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Breaking RSA Encryption Protocol with Kernel P Systems

Vasile

Gheorghe

Niculescu

2023

Preprint

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The prime factorisation problem is intractable, i.e., no efficient algorithm is known. In cryptography there are some well-known approaches based on the computational hardness of this problem, including Rivest-Shamir-Adleman (RSA) encryption protocol. Several attemps to break RSA have been investigated, some of them based on the massive parallelism of membrane systems.In this paper a new approach, based on kernel P system formalism, aimed at reassessing the space-time tradeoff, usually involved in membrane computing solutions, is investigated. Two models are introduced and assessed in order to find the potential benefits of each of them for solving the above problem, revealing also their limitations and providing hints for further improvements.

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Section: Introductionmentioning

confidence: 99%

Breaking RSA Encryption Protocol with Kernel P Systems

Vasile

Gheorghe

Niculescu

2023

Preprint

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Integrated dynamic spiking neural P systems for fault line selection in distribution network

Ma,

Yang,

Zhang

et al. 2024

Nat Comput

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Implementing perceptrons by means of water-based computing

Civiero,

Henderson,

Hinze

et al. 2024

J Membr Comput

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Water-based computing emerged as a branch of membrane computing in which water tanks act as permeable membranes connected via pipes. Valves residing at the pipes control the flow of water in terms of processing rules. Resulting water tank systems provide a promising platform for exploration and for case studies of information processing by flow of liquid media like water. We first discuss the possibility of realizing a single layer neural network using tanks and pipes systems. Moreover, we discuss the possibility to create a multi-layer neural network, which could be used to solve more complex problems. Two different implementations are considered: in a first solution, the weight values of the connections between the network nodes are represented by tanks. This means that the network diagram includes multiplication structures between the weight tanks and the input tanks. The second solution aims at simplifying the network proposed in the previous implementation, by considering the possibility to modify the weight values associated to neuron by varying the diameter of the connecting pipes between the tanks. The multiplication structures are replaced with a timer that regulates the opening of the outlet valves of all the tanks. These two implementations can be compared to evaluate their efficiency, and considerations will be made regarding the simplicity of implementation.

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Deep dynamic spiking neural P systems with applications in organ segmentation

Cited by 6 publications

References 36 publications

Breaking RSA Encryption Protocol with Kernel P Systems

Breaking RSA Encryption Protocol with Kernel P Systems

Integrated dynamic spiking neural P systems for fault line selection in distribution network

Implementing perceptrons by means of water-based computing

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