A survey of cellular automata: types, dynamics, non-uniformity and applications

Bhattacharjee, Kamalika; Naskar, Nazma; Roy, Samir; Das, Sukanta

doi:10.1007/s11047-018-9696-8

Cited by 70 publications

(39 citation statements)

References 276 publications

(334 reference statements)

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“…Hücresel otomatlar ilk olarak biyolojik ve fiziksel sistemlerin modellenmesinde kullanıldı. 1980'lerden sonra gerçek hayat problemleri üzerinde kullanılmaya başlamıştır [22].…”

Section: Yöntemle İlgili çAlışmalar (Methods Related Work)unclassified

Hücresel Otomata Tabanlı İkili Sınıflandırma

Peldek

Becerikli

2019

Bilişim Teknolojileri Dergisi

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Özet-Bu çalışmada lineer olmayan hücresel otomatlar ikili örüntü sınıflandırmada kullanılmıştır. Hücresel otomatlar ilk olarak günümüz bilgisayar mimarisinin çalışma prensiplerini belirleyen Von Neumann tarafından biyolojik canlıların kendini yenileme yeteneklerini modellemek için önerilmiştir. Hücresel otomatlar, ızgara düzlemindeki hücrelerin etrafındaki hücrelerle etkileşime bağlı durum güncelleme mantığına dayanan hesaplama modeline sahiptir. Hücresel otomatlar üzerine yapılan çalışmalarda bazı durumların diğer durumlarla dinamik bir etkileşim içerisinde olduğu gözlemlendi. Bu durumlar diğer durumları etrafında toplayan ve çekim merkezi formunda hareket eden davranışlar sergiliyorlardı. Çekim merkezi formunda hareket eden durumlara cezbedici durum(veya cezbedici kap) adı verildi. Cezbedicilerin dinamik davranışları, bir örüntünün diğer örüntüleri çekmesi olarak ele alınması hücresel otomatların örüntü tanıma ve sınıflandırmada kullanma potansiyelini ortaya çıkarmıştır. Hücresel otomata tabanlı ilk örüntü tanıma yöntemleri XOR ve XNOR mantığına göre durum güncellemesi yapan kurallar kullanan lineer yöntemlerdir. Daha sonra lineer yöntemlerin kısıtlarını aşmak için lineer olmayan yöntemler geliştirilmiştir. Bu çalışmada cezbedici durumların nitelenmesi için erişilebilirlik ağacı tabanlı lineer olmayan yöntemler kullanılmıştır. Cezbedici durumlar farklı veri setleri üzerinde ikili sınıflandırma amacı ile kullanılmıştır. Elde edilen sonuçlar daha önce yapılan hücresel otomata tabanlı örüntü tanıma yöntemleri ve diğer yöntemlerle karşılaştırılmıştır.Anahtar Kelimeler-hücresel otomata, çoklu cezbedici hücresel otomatlar, ikili sınıflandırma Cellular Automata Based Binary ClassificationAbstract-In this study, nonlinear cellular automata were used for binary pattern classification. Cellular automata were first proposed by Von Neumann, who determined the working principles of today's computer architecture, to model the self-renewal abilities of biological beings. Cellular automata have a computational model based on the state update logic due to the interaction with the cells around the cells in the grid plane. Studies on cellular automata have shown that some states are in a dynamic interaction with other states. These states gathered other states around itself and acted in the centre of attraction. States that behave in the form of attraction centre are called attractor state(or attractor basin). The dynamic behaviours of the attractor were considered as a pattern of attracting other patterns and revealed the potential of using cellular automata in pattern recognition and classification. The first pattern recognition methods based on cellular automata are linear methods that use rules that update the state according to XOR and XNOR logic. Later nonlinear methods have been developed to overcome the limitations of linear methods. In this study, reachability tree based nonlinear methods are used to characterize the attractors. Attractor states are used for binary classification purposes on different data sets. The resu...

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“…Hücresel otomatlar ilk olarak biyolojik ve fiziksel sistemlerin modellenmesinde kullanıldı. 1980'lerden sonra gerçek hayat problemleri üzerinde kullanılmaya başlamıştır [22].…”

Section: Yöntemle İlgili çAlışmalar (Methods Related Work)unclassified

Hücresel Otomata Tabanlı İkili Sınıflandırma

Peldek

Becerikli

2019

Bilişim Teknolojileri Dergisi

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“…Another interesting aspect of using cellular automata is that both uniformity and non-uniformity can be modeled. A cellular automaton is defined as [3] (d, L, S, N, f ), where d represents the cellular space dimension, L represents the cellular space, S is the finite set of states, N is the neighborhood vector and f or (f 1 ,f 2 ,...) is the local rule or ruleset respectively. The global rule of the cellular automata is designated by Φ.…”

Section: Background On Cellular Automatamentioning

confidence: 99%

“…CAs are widely used in cryptography and other fields to benefit from their simplicity, parallelism, and unpredictability. Besides, CAs make the hardware and software implementations easier [3]. In this paper, a new design called CA-PCS (Cellular Automata based Partition Ciphering System) is proposed.…”

Section: Introductionmentioning

confidence: 99%

CA-PCS: A Cellular Automata based Partition Ciphering System

Ziani¹,

Sadak²,

Hanin³

et al. 2020

IJACSA

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In this paper, the authors present a modified version of the Partition Ciphering System (PCS) encryption system previously proposed. The previously developed encryption system PCS uses the partition problem to encrypt a message. The goals of newly developed system are avoiding statistical and frequency attacks, by providing a balance between 0s and 1s, ensuring a good level of entropy and achieving confidentiality through encryption. One of the novelties of the new design compared to its predecessor is the use of cellular automata (CAs) during the encryption. The use of CAs is justified by their good cryptographic properties that provide a level of security against attacks, and better confusion and diffusion properties. The new design is first presented with details of the encryption and decryption mechanisms. Then, the results of the DIEHARDER battery of tests, the results of the avalanche test, a security analysis and the performance of the system are outlined. Finally, a comparison between CA-PCS and PCS as well as the AES encryption system is provided. The paper shows that the modified version of PCS displays a better performance as well as a good level of security against attacks.

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“…Though the precise mechanism of how computation is derived from propagation is unknown, one possibility is that it is the result of what one might consider to be 'information collisions.' This idea is based on the finding of Lizier et al (2010) who demonstrated that the dominant form of information modification (i.e., computation) in cellular automata are the result of collisions between the emergent particles (see also Adamatzky and Durand-Lose, 2012;Bhattacharjee et al, 2016;Sarkar, 2000). In the context of our circuits, the idea is that computation arises when packets of information embedded in the outgoing spike trains of sending neurons collide onto the same receiving neuron in sufficient temporal proximity to alter the way the receiver responds to those inputs.…”

Section: Rich Clubs As a Home For Computationmentioning

confidence: 99%

Computation is concentrated in rich clubs of local cortical neurons

Faber

Timme

Beggs

et al. 2018

Preprint

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To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit topology. Rich-clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich-clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average ~300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute ~200% more information than neurons outside of the rich club. Indeed, the amount of computation performed in the rich-club was proportional to the amount information propagation by the same neurons. This suggests that, in these circuits, information propagation drives computation. Comparing the computation-to-propagation ratio inside versus outside of the rich club showed that rich clubs compute at a slightly, though significantly, reduced level (~4% lower). In total, our findings indicate that rich club topology in effective cortical circuits supports not only information propagation but also neural computation. AUTHOR SUMMARYHere we answer the question of whether rich club topology in functional cortical circuits supports neural computation as it has been previously shown to do for information propagation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that neurons in rich clubs computed significantly more than neurons outside of rich clubs, suggesting that rich-clubs do support computation in cortical circuits. Indeed, the amount of computation that we found in the rich club was proportional to the amount of information they propagate suggesting that, in these circuits, information propagation drives computation.

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A survey of cellular automata: types, dynamics, non-uniformity and applications

Cited by 70 publications

References 276 publications

Hücresel Otomata Tabanlı İkili Sınıflandırma

Hücresel Otomata Tabanlı İkili Sınıflandırma

CA-PCS: A Cellular Automata based Partition Ciphering System

Computation is concentrated in rich clubs of local cortical neurons

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