Computation in Cellular Automata: A Selected Review

Mitchell, Melanie; Gramss, In T.; Bornholdt, Stefan; Gross, Mark D.; Pellizzari, T.

doi:10.1002/3527602968.ch4

Cited by 111 publications

(80 citation statements)

References 62 publications

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“…∆ Q in the above definitions is the space of probability distributions Q that have the same pairwise marginal distributions between each input and the output as the original joint distribution P of X 1 , X 2 , Y, i.e. : [18,19]. In this study, specifically, we will focus on information modification, yet we first need to decompose the output variable in terms of information storage and information transfer, where the latter will also contain the information modification (see [11] and Figure 2):…”

Section: Definition and Estimation Of Unique Shared And Synergistic mentioning

confidence: 99%

Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

Wibral

Finn²,

Wollstadt³

et al. 2017

Entropy

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Information processing performed by any system can be conceptually decomposed into the transfer, storage and modification of information-an idea dating all the way back to the work of Alan Turing. However, formal information theoretic definitions until very recently were only available for information transfer and storage, not for modification. This has changed with the extension of Shannon information theory via the decomposition of the mutual information between inputs to and the output of a process into unique, shared and synergistic contributions from the inputs, called a partial information decomposition (PID). The synergistic contribution in particular has been identified as the basis for a definition of information modification. We here review the requirements for a functional definition of information modification in neuroscience, and apply a recently proposed measure of information modification to investigate the developmental trajectory of information modification in a culture of neurons vitro, using partial information decomposition. We found that modification rose with maturation, but ultimately collapsed when redundant information among neurons took over. This indicates that this particular developing neural system initially developed intricate processing capabilities, but ultimately displayed information processing that was highly similar across neurons, possibly due to a lack of external inputs. We close by pointing out the enormous promise PID and the analysis of information modification hold for the understanding of neural systems.

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Section: Definition and Estimation Of Unique Shared And Synergistic mentioning

confidence: 99%

Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

Wibral

Finn²,

Wollstadt³

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…To date, they have used this way only for extremely simple con guration mappings, mappings which can be trivially learned by other kinds of systems. Despite the simplicity of these mappings, the use of genetic algorithms to try to train cellular automata to exhibit them has achieved little success 61,62,191,192].…”

Section: Training Cellular Automata With Genetic Algorithmsmentioning

confidence: 99%

Theory of Collective Intelligence

Wolpert

2004

Collectives and the Design of Complex Systems

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This paper surveys the emerging science of how to design a \COllective INtelligence" (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized, personalized communication and/or control. ii) There is a provided world utility function that rates the possible histories of the full system.In particular, we a r e i n terested in COINs in which e a c h a g e n t runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they \cooperate" as far as the world utility i s concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we w i s h t o s o l v e the COIN design problem implicitly, via the \adaptive" character of the RL algorithms of each of the agents. This approach i n troduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we d o n o t have phenomena like t h e tragedy of the commons, Braess's paradox, or the liquidity trap? Although still very young, research speci cally concentrating on the COIN design problem has already resulted in successes in arti cial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scienti c elds, such as economics, game theory, and population biology.

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“…They are specially suitable for modeling natural systems that can be described as massive collections of simple objects interacting locally with each other. 28,29 The cells update their states synchronously on discrete steps according to a local rule. The new state of each cell depends on the previous states of a set of cells, including the cell itself, and constitutes its neighborhood.…”

Section: Cellular Automatamentioning

confidence: 99%

Cellular Learning Automata Based Dynamic Channel Assignment Algorithms

Beigy

Meybodi

2009

Int. J. Comp. Intel. Appl.

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A solution to channel assignment problem in cellular networks is self-organizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status of channels in the whole network will be used by cells for channel assignment whereas in the third algorithm, the additional information regarding status of channels may be gathered and then used by cells in order to allocate channels. The simulation results show that by using the proposed channel assignment algorithms the micro-cellular network can self-organize itself. The simulation results also show that the additional information used by the third algorithm help the cellular learning automata to find an assignment which results in lower blocking probability of calls for the network.

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Computation in Cellular Automata: A Selected Review

Cited by 111 publications

References 62 publications

Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

Theory of Collective Intelligence

Cellular Learning Automata Based Dynamic Channel Assignment Algorithms

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