Universal computation in fluid neural networks

Solé, Ricard V.; Delgado, Jordi

doi:10.1002/(sici)1099-0526(199611/12)2:2<49::aid-cplx13>3.0.co;2-t

Cited by 23 publications

(10 citation statements)

References 18 publications

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“…Instead of attempting to track each change, the organism can adopt a single behavior that maximizes the average long-term benefit. Yet, despite these differences both studies show that the decision making in ants and slime molds can be understood as instances of the same phenomenon: behavior selection as stochastic attractor switching [28]. …”

Section: Discussionmentioning

confidence: 99%

The role of noise in self-organized decision making by the true slime mold Physarum polycephalum

2017

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Self-organized mechanisms are frequently encountered in nature and known to achieve flexible, adaptive control and decision-making. Noise plays a crucial role in such systems: It can enable a self-organized system to reliably adapt to short-term changes in the environment while maintaining a generally stable behavior. This is fundamental in biological systems because they must strike a delicate balance between stable and flexible behavior. In the present paper we analyse the role of noise in the decision-making of the true slime mold Physarum polycephalum, an important model species for the investigation of computational abilities in simple organisms. We propose a simple biological experiment to investigate the reaction of P. polycephalum to time-variant risk factors and present a stochastic extension of an established mathematical model for P. polycephalum to analyze this experiment. It predicts that—due to the mechanism of stochastic resonance—noise can enable P. polycephalum to correctly assess time-variant risk factors, while the corresponding noise-free system fails to do so. Beyond the study of P. polycephalum we demonstrate that the influence of noise on self-organized decision-making is not tied to a specific organism. Rather it is a general property of the underlying process dynamics, which appears to be universal across a wide range of systems. Our study thus provides further evidence that stochastic resonance is a fundamental component of the decision-making in self-organized macroscopic and microscopic groups and organisms.

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Section: Discussionmentioning

confidence: 99%

The role of noise in self-organized decision making by the true slime mold Physarum polycephalum

2017

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“…This also allows natural communities to adapt to changing environments but is a fundamental challenge in synthetic biology, as will be discussed below. However, the merits of liquid networks have been investigated (Langton, 1986;Miramontes et al, 1993;Solé and Miramontes, 1995;Solé and Delgado, 1996;Vining et al, 2019) and it has been shown that liquid networks are capable of reaching a global consensus (Vining et al, 2019) and universal computation (Solé and Delgado, 1996).…”

Section: Differences Between Biological and Silicon Systemsmentioning

confidence: 99%

From Microbial Communities to Distributed Computing Systems

Karkaria¹,

Treloar²,

Barnes³

et al. 2020

Front. Bioeng. Biotechnol.

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A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

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“…However, examples of universal computers have been far less common, though diverse, based for example on silicon chips, DNA, neural nets, collective behavior, molecular arrays, quantum mechanics, collision systems and specialized fluid-flow geometries (e.g. Siegelmann and Sontag, 1995; Solé and Delgado, 1996; Nielsen and Chuang, 1997; Adamatzky, 2002; Benenson and others, 2004; De Silva and others, 2006; Prakash and Gershenfeld, 2007). Because of the search for practical devices, most known universal systems are microscopic, and in most cases the construction of general-purpose computation is by design and is rarely an accidental by-product.…”

Section: Glaciers As Information Processingmentioning

confidence: 99%

On fundamental limits to glacier flow models: computational theory and implications

Bahr¹

2009

J. Glaciol.

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No single flow model can simulate all possible glaciers and ice sheets without violating fundamental tenets of computational science. The root cause is not one of numerical sophistication, precision or accurate initial conditions. Instead, using flow and transport as data transmission, glaciers inadvertently function as information processors. This computational capability confers a level of complexity that inherently limits our ability to accurately and efficiently predict glacier flow and therefore, for example, to forecast those aspects of climate systems that depend on glaciers. In particular, even with considerable future advancements in glacier physics, computational theory shows that no dramatic improvements in numerical speed are likely when compared to today’s glacier models. Therefore, to increase speed and resolution, the next generation of climate and sea-level models must rely on simulations tailored to specific ice-sheet geometries rather than general-purpose glacier flow models. However, because glaciers process information, entirely new computation-theoretic advances in glaciology are possible, and concepts from information entropy may help to define new glacier scaling relationships and identify which geometries will be most problematic for modeling.

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Universal computation in fluid neural networks

Cited by 23 publications

References 18 publications

The role of noise in self-organized decision making by the true slime mold Physarum polycephalum

The role of noise in self-organized decision making by the true slime mold Physarum polycephalum

From Microbial Communities to Distributed Computing Systems

On fundamental limits to glacier flow models: computational theory and implications

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