Bacterial linguistic communication and social intelligence

Jacob, Eshel Ben; Becker, Israela; Shapira, Yoash; Levine, Herbert

doi:10.1016/j.tim.2004.06.006

Cited by 249 publications

(227 citation statements)

References 39 publications

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“…Although the results described in this Letter were obtained in the true slime mold, more primitive organisms, such as bacteria, can demonstrate intelligent behavior with a simple mechanism in terms of nonlinear dynamics [16]. The nonlinear dynamics perspective might thus be the key to revealing the secret of how biological systems overcome challenges to their survival.…”

Section: Fig 3 Sps Responses Over a Range Of Stimulation Periods ( )mentioning

confidence: 93%

Amoebae Anticipate Periodic Events

et al. 2008

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When plasmodia of the true slime mold Physarum were exposed to unfavorable conditions presented as three consecutive pulses at constant intervals, they reduced their locomotive speed in response to each episode. When the plasmodia were subsequently subjected to favorable conditions, they spontaneously reduced their locomotive speed at the time when the next unfavorable episode would have occurred. This implied the anticipation of impending environmental change. We explored the mechanisms underlying these types of behavior from a dynamical systems perspective. DOI: 10.1103/PhysRevLett.100.018101 PACS numbers: 87.17.Aa, 87.18.Hf, 87.23.Kg Information processing is an interesting component of biological systems. Although the brain has evolved to perform this specific function, information processing is possible without a brain, and organisms as simple as amoebae are much more intelligent than generally thought. For example, the true slime mold Physarum polycephalum can solve a maze and certain geometrical puzzles, in order to satisfy its needs for efficient absorption of nutrients and intracellular communication [1][2][3][4]. Thus, from an evolutionary perspective, information processing by unicellular organisms might represent a simple precursor of braindependent higher functions. Anticipating and recalling events are two such functions; however, the way in which they self-organize has so far remained unknown.P. polycephalum is a useful model organism for studying behavioral intelligence [5]. Its plasmodium is a large aggregate of protoplasm that possesses an intricate network of tubular structures, and can crawl over agar plates at a speed of approximately 1 cm=h at room temperature. In order to investigate primitive forms of brain function (such as learning, memory, anticipation, and recall), here we have examined the rhythmicity of cell behaviors [6,7] and the adaptability of cells to periodic environmental changes [8,9]. Our approach was to expose organisms to periodic changes in ambient conditions and to observe their behavioral responses. As there has been much controversy in recent years over the existence of nonhuman intelligence [10 -13], this subject requires evaluation by modern scientific techniques.Here we show that an amoeboid organism can anticipate the timing of periodic events. Moreover, we explore the mechanisms underlying this behavior from a dynamical systems perspective. Our results hint at the cellular origins of primitive intelligence, and imply that simple dynamics might be sufficient to explain its emergence.A large plasmodium of P. polycephalum was cultured with oat flakes in a trough (25 35 cm 2 ) on an agar plate under dim light. The tip region of an extended front was removed and placed in a narrow lane (0:5 28 cm 2 ) at 26 C and 90% humidity (hereafter referred to as standard conditions; all experiments were conducted under these ambient conditions unless otherwise specified). The organism migrated along the lane, and the position of its tip was measured every 10 min. After migrat...

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Section: Fig 3 Sps Responses Over a Range Of Stimulation Periods ( )mentioning

confidence: 93%

Amoebae Anticipate Periodic Events

et al. 2008

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“…A particularly promising species with which to conduct such experiments is bacteria, which engage in remarkably varied and sophisticated behaviors (19,(39)(40)(41)(42)(43)(44)(45)(46). Although an individual bacterium is clearly mindless, colonies of bacteria, such as Paenibacillus vortex, have been observed to engage in seemingly intelligent behavior, such as competition, collaborative foraging, and cell-to-cell chemotactic and physical communication (19).…”

Section: Testable Implicationsmentioning

confidence: 99%

The origin of risk aversion

Zhang

Brennan

2014

Proc. Natl. Acad. Sci. U.S.A.

107

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Risk aversion is one of the most basic assumptions of economic behavior, but few studies have addressed the question of where risk preferences come from and why they differ from one individual to the next. Here, we propose an evolutionary explanation for the origin of risk aversion. In the context of a simple binary-choice model, we show that risk aversion emerges by natural selection if reproductive risk is systematic (i.e., correlated across individuals in a given generation). In contrast, risk neutrality emerges if reproductive risk is idiosyncratic (i.e., uncorrelated across each given generation). More generally, our framework implies that the degree of risk aversion is determined by the stochastic nature of reproductive rates, and we show that different statistical properties lead to different utility functions. The simplicity and generality of our model suggest that these implications are primitive and cut across species, physiology, and genetic origins.risk aversion | risk preferences | expected utility theory | risk-sensitive foraging | evolution R isk aversion is one of the most fundamental properties of human behavior. Ever since pioneering work by Bernoulli (1) on gambling and the St. Petersburg Paradox in the 17th century, considerable research has been devoted to understanding human decision-making under uncertainty. Two of the most well-known theories are expected utility theory (2) (an axiomatic formulation of rational behavior under uncertainty) and prospect theory (3) (a behavioral theory of decision-making under uncertainty). Several measures of risk aversion have been developed, including curvature measures of utility functions (4, 5), human subject experiments and surveys (6, 7), portfolio choice for financial investors (8), labor-supply behavior (9), deductible choices in insurance contracts (10,11), contestant behavior on game shows (12), option prices (13), and auction behavior (14).Despite its importance and myriad applications in the past several decades, few economists have addressed the question: where does risk aversion come from? Biologists and ecologists have documented risk aversion in nonhuman animal speciesoften called risk-sensitive foraging behavior-ranging from bacteria to primates (15)(16)(17)(18)(19). Recently, the neural basis of risk aversion has also received much attention, because researchers discovered that the activity of a specific brain region correlates with risktaking and risk-averse behavior (20)(21)(22).Evolutionary principles have been applied by economists to a variety of economic behaviors and concepts, including altruism (23, 24), the rate of time preference (25), and utility functions (26-29) † . In particular, Robson (26) proposes an evolutionary model of risk preferences, in which he assumes an increasing concave relation between an individual's number of offspring and the amount of resources available to that individual, and given this concave "biological production function," Robson (26) shows that expected utility arises from idiosyncratic environme...

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“…For each species, the bacterial colonies generate various patterns depending on surrounding environments, mainly, including the nutrient concentration and the softness of the medium. This cooperative behavior in adaptation to survival in the environment reflects bacterial communication capabilities and social intelligence [13,15]. The understanding of the underlying mechanism is not only important to the biotechnology but also the basic science of the living organisms.…”

Section: Introductionmentioning

confidence: 99%

Self-similar solutions to a density-dependent reaction-diffusion model

Ngamsaad¹,

Khompurngson²

2012

Phys. Rev. E

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In this paper, we investigated a density-dependent reaction-diffusion equation, ut = (u m )xx + u − u m . This equation is known as the extension of the Fisher or Kolmogoroff-Petrovsky-Piscounoff equation which is widely used in the population dynamics, combustion theory and plasma physics. By employing the suitable transformation, this equation was mapped to the anomalous diffusion equation where the nonlinear reaction term was eliminated. Due to its simpler form, some exact self-similar solutions with the compact support have been obtained. The solutions, evolving from an initial state, converge to the usual traveling wave at a certain transition time. Hence, it is quite clear the connection between the self-similar solution and the traveling wave solution from these results. Moreover, the solutions were found in the manner that either propagates to the right or propagates to the left. Furthermore, the two solutions form a symmetric solution, expanding in both directions. The application on the spatiotemporal pattern formation in biological population has been mainly focused.

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Bacterial linguistic communication and social intelligence

Cited by 249 publications

References 39 publications

Amoebae Anticipate Periodic Events

Amoebae Anticipate Periodic Events

The origin of risk aversion

Self-similar solutions to a density-dependent reaction-diffusion model

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