Perspectives on Computation
in Plants
Abstract Plants thrive in virtually all natural and human-adapted
environments and are becoming popular models for developing
robotics systems because of their strategies of morphological and
behavioral adaptation. Such adaptation and high plasticity offer new
approaches for designing, 造型, and controlling artificial
systems acting in unstructured scenarios. 同时, 这
development of artifacts based on their working principles reveals
how plants promote innovative approaches for preservation
and management plans and opens new applications for
engineering-driven plant science. Environmentally mediated growth
图案 (例如, tropisms) are clear examples of adaptive behaviors
displayed through morphological phenotyping. Plants also create
networks with other plants through subterranean roots–fungi
symbiosis and use these networks to exchange resources or warning
signals. This article discusses the functional behaviors of plants and
shows the close similarities with a perceptron-like model that could
act as a behavior-based control model in plants. We begin by
analyzing communication rules and growth behaviors of plants; 我们
then show how we translated plant behaviors into algorithmic
solutions for bioinspired robot controllers; and finally, we discuss
how those solutions can be extended to embrace original approaches
to networking and robotics control architectures.
Emanuela Del Dottore
Bioinspired Soft Robotics Laboratory
Istituto Italiano di Tecnologia
emanuela.deldottore@iit.it
Barbara Mazzolai
Bioinspired Soft Robotics Laboratory
Istituto Italiano di Tecnologia
barbara.mazzolai@iit.it
关键词
Behavior, 控制, bioinspiration,
perceptron, 网络, adaptation
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1 介绍
Plants have inspired researchers in many fields, including robotics, for the development of ar-
tificial growing robots (Del Dottore, Sadeghi, 等人。, 2018) or continuum robots (沃克, 2015)
and in informatics to develop novel heuristics for search and optimization problems (Akyol &
Alatas, 2017; L. Ma et al., 2015). 然而, because of their apparent immobility and lack of brain,
there are frequently animated debates concerning possible forms of intelligence in plants (Cvrˇcková
等人。, 2009; Firn, 2004; Robinson et al., 2020; Trewavas, 2003). Most definitions of intelligence
(Legg & Hutter, 2007) refer to the ability of a system to adapt to variable conditions in the surround-
ing environment. Disagreements about plant intelligence are probably due to the different means of
adaptation in nature across the animal (a widely accepted model in computation and robotics) 和
plant kingdoms. In some animals, but apparently not in plants, adaptation is manifested in learning
(forming associations of ideas necessary for survival) (Estes, 2014). Plants adapt to their environ-
ments by continuously modifying their physiologies and morphologies, known as plant plasticity
or phenotypic plasticity. This is very different from the strengthening of synaptic connections,
那是, synaptic plasticity, that takes place during learning processes in cognitive animals (Estes,
2014). In biology, adaptation refers to any process by which a system modifies itself to improve
© 2023 麻省理工学院.
根据知识共享署名发布
4.0 国际的 (抄送 4.0) 执照.
Artificial Life 29: 336–350 (2023) https://doi.org/10.1162/artl_a_00396
乙. Del Dottore and B. Mazzolai
Computation in Plants
performance in its environment (Holland, 1992). This involves the transmission of information
from the environment to the organism through a selection of phenotyping or behavioral alter-
natives (Estes, 2014). On the basis of this definition, plants are organisms that act intelligently.
Through evolution, they continuously improve their adaptive strategies to survive in mutable en-
vironments (Gratani, 2014). The environment is central to plant life, 发展, and evolution.
Different ecological niches have generated many species variations with specialized physiological
and morphological adaptation mechanisms. Preprogrammed genetics is shaped in real time by envi-
ronmental stimuli. Plants and other organisms release traces into the environment that impact latent
plant plasticity. Intelligence is forged by the interactions between plants and the environment. 那里
is thus a clear analogy between intelligent behavior in plants and the approach to artificial intelli-
gence and robotics proposed by Rodney Brooks (1999). Brooks suggested that intelligent behavior
exists only in the observer’s eye as a phenomenon resulting from the interactions among multi-
ple perception-action modules that make up the control architecture of an embodied and situated
系统 (Arkin, 1998). He suggested the existence of behaviors, not produced by a high-level cog-
nitive process (implying representation or reasoning), but emerging from the interactions between
body and environment. The way the system behaves modifies the environment where it is situated
和, 最后, its future perception-actions. His view has led to a modern approach to artificial
intelligence called new AI (布鲁克斯, 1999), which emphasizes the embodiment of behaviors and the
unavoidable interdependence of a physical system with its environment (弗洛雷亚诺 & Mattiussi, 2008;
Scheier & 普法伊费尔, 1999). The concepts Brooks proposed then evolved over multiple paths. From a
control theory perspective, several schemas have been proposed in behavior-based control architec-
特雷斯. They differ in how behaviors are combined or suppressed to generate purely reflexive systems
or hybrid architectures, in which reactive behaviors might be complemented with a deliberative part
(Arkin, 1998). From a physical perspective, the shaping of system behavior mediated by environ-
ment interactions has converged into embodied intelligence and morphological control concepts
(普法伊费尔 & Gómez, 2009; 普法伊费尔 & Scheier, 2001). According to these theories, a physical system
embodies part of its computation by exploiting mechanical properties and material nonlinearities
to lighten the complexity of robot control. These theories express body-integrated behavior-based
controls, with behaviors combined by design. Purposeful design and choice of components and
materials are keys to programming such systems. Nature has designed embodied intelligent systems,
reaching maximal performance with plants. Plants have thus been taken as a source of inspiration
to mimic nastic movements into functional materials (Sydney Gladman et al., 2016) 或者, 例如,
to embody inherent adaptation to heterogenous environments in a growing robot based on addi-
tive manufacturing (Sadeghi et al., 2020). Reflexive adaptation in plants, 在某些情况下, precedes a
subsequent active information processing with different levels of complexity that can be captured
in computational models. Following this idea, computation in plants is the perception processed
through multiple interacting behavioral modules or perception-action nodes, with an intelligence
emerging from these interactions. This article presents examples of different adaptive behavioral
strategies of plants (部分 2). We first provide an abstraction of the concept of plant computational
architecture by discussing selected plant behaviors, 那是, tropisms, and show how we translated
them into computational models (部分 3). We then discuss how to extend the proposed abstrac-
tion to capture computation at different levels of the plant hierarchy, up to a community of plants,
briefly discussing the case study of plant-to-plant communication mediated by mycorrhizal networks
(部分 4). We conclude with some final remarks on achievements, future directions and possible
applications of plant-inspired control strategies (部分 5). This perspective serves as a guideline
for the engineering interpretation of plant intelligent behaviors for bioinspired robotic control and
network models.
2 Behaviors in Plants
Plant behavior is the phenotypic plasticity expressed in response to external events during a plant’s
寿命. Plants are multicellular organisms living in communities and have hierarchical structures
Artificial Life Volume 29, 数字 3
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乙. Del Dottore and B. Mazzolai
Computation in Plants
数字 1. (A) Hierarchical structure of a plant, starting from its internal arrangement and moving to its organization in
社区, where underground mycorrhizal networks interconnect with multiple organisms. (乙) Apical growth in
植物. Roots and shoots have a meristematic region where cells divide. The cells produced start to elongate, pushing
the apex forward (light blue arrows). The elongation of cells is pronounced in the elongation region and stops in the
maturation region, where cell specialization for secondary growth occurs. (C) Examples of different behaviors observed
in plants. In the organs: circumnutations, stabilization, seedling, search for symbiosis. In the organism: apical dominance.
In the community: social defense mechanisms.
(Ferlian et al., 2018; Jha et al., 2021) with regular patterns, 模块化, and spatial organization
(Dupuy et al., 2008; Sachs, 1991). Multiple organs (例如, roots and leaves) constitute a single plant.
Organs are organized into tissues, formed by cells, where molecules undergo physiological processes
instantiating internal rhythms, propagating from within the cells and crossing the higher layers of
the hierarchy (图1a). Mutual interactions of the multiple layers define regulatory mechanisms
of plant functioning and behavior.
Plants have evolved several adaptive behaviors to counteract their sessile lifestyle at all levels
of their hierarchy. Because of their sessility, their strategies are very different from those used by
动物, the main one being apical growth (Figure 1b). Although plants do not locomote, they move
by growing, with cell division and elongation occurring at their apices of roots and aerial branches
for their entire lives. Based on growth, plants implement various movements to explore, navi-
门, and colonize their environment. Two key examples of growth-driven movements are tropisms,
which are irreversible directed growth responses to external stimuli (哈特, 1990; Muthert et al.,
2020), and circumnutations, which are reversible oscillatory growth-driven movements adopted, 为了
例子, in the shoots of climbers to explore their surroundings and find mechanical support
(Stolarz, 2009) (Figure 1c). Growth-driven movements affect organs and portions of organs (例如,
apical parts where cell division and elongation occur, named meristematic regions) (Perilli et al.,
2012). 本质上, directed growth is a form of foraging—hydrotropism (foraging for water),
chemotropism (foraging for nutrients), phototropism (foraging for light) (Ballaré et al., 1997;
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Artificial Life Volume 29, 数字 3
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乙. Del Dottore and B. Mazzolai
Computation in Plants
Cahill & McNickle, 2011)—and oscillatory growth is a form of exploratory behavior (例如,
circumnutations in shoots of twiners) (Stolarz, 2009). Aerial branches differ from roots. 对于前-
充足, phototropism is positive in the shoot and negative in roots, and within the same apparatus,
there are priorities dictated genetically with a preprogrammed architecture that are adjusted dynam-
ically with the expressed phenotype (Cahill et al., 2010; 霍奇, 2004) (Figure 1c). Dominance hier-
archy behavior appears among organs of the same individual, leading to a single apical dominance
to reach faster growth and higher resource allocation (Aloni et al., 2006) (Figure 1c). 在这种情况下, 这
observed behavior is at the level of the plant, which invests energy in the most promising searching
器官. The symbiotic relationship between plant roots and fungal mycorrhizae (Ferlian et al., 2018)
is another key adaptation strategy. The plant exploits this symbiosis to procure phosphorous, 之中
other mineral nutrients, and water, and the fungus uses the plant to gain carbon.
This symbiosis manifests intelligence in the form of social behaviors (Figure 1c), such as collab-
orative sharing of resources (Whiteside et al., 2019), warning of biotic attacks (Cabral et al., 2019),
competitive behaviors for the acquisition of resources (Selosse et al., 2006), and conquering an area
(Stinson et al., 2006). Plant roots release and perceive exudates in their rhizosphere (Bais et al.,
2006), similarly to volatile compounds released from the aerial part (Holopainen & Gershenzon,
2010). These molecules act as traces to communicate indirectly with other plant species, bacteria,
fungal hyphae, 动物, or insects, to warn about biotic attacks or compensate for abiotic stresses
within a community of related plants. These examples of intelligent behaviors suggest cognitive
processes in plants and have sparked considerable interest in the scientific community. 考试用-
普莱, plant neurobiology (Brenner et al., 2006; Segundo-Ortin & Calvo, 2022) is a new discipline to
understand how plants process information. Forms of cognitive processes, 学习, and memory in
plants have been identified, highlighting the information-processing network implemented over dis-
tributed computing cells (Calvo Garzon, 2007; Mediano et al., 2021; Parise et al., 2020) and displayed
in anticipatory behaviors (Goodspeed et al., 2013; Latzel & Münzbergová, 2018). Well-accepted an-
ticipatory behaviors of plants are those connected to the circadian clock, such as the opening and
closing of some flowers and their leaves, anticipating sunshine to regulate their exposure to light
(施瓦茨 & Koller, 1986). Another strategy is being able to anticipate herbivore attacks. Plants can
differentiate between the sounds of chewing, 风, and insects and prime their defense responses
to vibrations induced by herbivore chewing in their proximity (Appel & Cocroft, 2014). Plants can
store and process data from the environment, integrating information over time (Meroz, 2021) 到
optimize acclimation and immune defense responses (Hilker & Schmülling, 2019). 最终, 全部
behaviors have a physiological basis at molecular and cellular levels (Aloni et al., 2006; Ferlian et al.,
2018; Salvi et al., 2020; Scheres & Van Der Putten, 2017). Behaviors are mediated by the perception
of the environment, with perception-action nodes distributed in plants at different levels of tissues
and cells. Perception-action nodes share similarities with sensory neurons (斯科特, 1992). 这些都是
cells connecting perception to motor cells (or motor neurons) through neural pathways composed
of other motor or internodes (Arber, 2012). It is justified to introduce sensory and motor neurons
while talking about plants if we recall that all biological cells can generate action potentials used for
signal transmission. These action potentials are generated by the migration of ions (例如, calcium)
across cell membranes when the concentration goes above an activation threshold (Aidley, 1998;
Volkov, 2012). Although plants do not have a nervous system, their cells trigger chemical reactions
similar to animals’ spiking neurons (K. Takahashi et al., 2021). It is not yet clear whether those
spikes also encode information used by plants, 例如, at distal parts. 尽管如此, 一些
the biosynthetic pathways underlying plant behaviors have been discovered. Tissue-dependent cal-
cium signatures (Krogman et al., 2020) 可能, 例如, downstream gravity-dependent growth
规定 (Huang et al., 2013) or govern nutrient homeostasis (Vigani & Costa, 2019).
3 Tropisms
Plants respond to a wide range of external stimuli, and tropisms are perhaps the most evident be-
havior in plants at the level of the organs. Tropisms are directed growth responses toward attractive
Artificial Life Volume 29, 数字 3
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乙. Del Dottore and B. Mazzolai
Computation in Plants
刺激 (例如, physical and chemical) from the environment or away from repulsive signals. For each
刺激, there is a corresponding tropism, 例如, gravitropism (response to gravity), hy-
drotropism (水), chemotropism (nutrients and salts), thigmotropism (mechanostimulation), 和
magnetotropism (response to magnetic fields). The same type of signal can even produce different
reactions in different organs of the same plant. 例如, roots grow toward gravity (积极的
gravitropism), while shoots display negative gravitropism by growing upward. Because this results
in an organ following an attractor, tropisms are essentially foraging behaviors in plants.
Tropic responses are essentially individual behaviors acting in parallel on the plant and connect-
ing sense to actuation, 那是, the directed growth of cells. In a previous study (Sadeghi et al., 2016),
our research group exploited this behavioral concept with a motor scheme as a behavior-based
建筑学 (Arkin, 1998) in plant-inspired robotic roots to enable biomimetic autonomous navi-
gation for environment exploration. Motor schemes are suitable for treating continuous responses
(Arkin, 1989, 1998). A robot can have an infinite space of potential reactions to its world, unlike
with subsumption architectures, where discrete behaviors (possible actions, discrete directions to
拿) must be defined and treated as discrete states of a finite state machine. Potential field methods
can be used to implement a continuous response. 通常, potential field methods are adopted to
define the world as a vector field space of repulsive signals to avoid obstacles (Arkin, 1998; Laue &
Röfer, 2004). In behavior-based systems, the vector field is evaluated only at the time of perception
and for the close surroundings of the robot. This solution limits the computational power required
to evaluate the vector field and allows for a fast sense–actuation coupled response. Plants do not
necessarily need to anticipate and avoid obstacles. 然而, a similar concept to repulsive vectors
can be postulated to define a field of attractive signals, whose preferential attractive direction can
be formulated as
y =
米(西德:2)
我=1
wixi − b,
(1)
where xi is the directed growth response for each of the external aggregated signals associated with
a specific behavior, i = gravitropism, hydrotropism, thigmotropism, chemotropism, 等等; wi
is the specific weight for the aggregated signal; and b is the specific activation threshold. A sum-
mation coordinates the incoming behaviors. Given this description, y represents the global directed
growth response entering into motor nodes to command cell elongation. This equation also refers
to a single-layer perceptron model (Arkin, 1998; 芦苇 & 分数, 1999), 哪个, 在这种情况下, is used for
direct association of perception with motor control. With this control, the robot activates indepen-
dent behaviors in parallel as soon as the corresponding incoming stimulus goes above the activation
临界点. Individual responses are summed together to obtain the attractive vector direction. 这
response, thus the direction the robot takes, depends only on the current perception, with no world
map reconstruction. In Sadeghi et al. (2016), we connected incoming environmental signals to mo-
tor control to achieve a directed differential elongation of robotic roots, seen as independent agents
of the plant. The robotic roots were equipped with humidity, temperature, and tactile sensors, 迪斯-
tributed around a conical tip and with an accelerometer. The roots were able to bend thanks to three
soft spring-based actuators (Figure 2a). An embedded board with a microcontroller was used to read
the sensors and connect perception to motor control (Figure 2b). 在这个例子中, all the agents are
identical, with wi fixed by the user and b set to zero. Different weights were assigned to the behav-
iors to analyze conditions to achieve different robot reactions to various settings and stimuli from
the surrounding world (Figure 2c). The robotic roots were developed to demonstrate plant roots’
exploratory and monitoring capabilities and to implement plantlike behaviors for application in soil
or air exploration. Such systems can be applied to reach a rapid localization of attractor sources,
例如, for underground water source detection or localization of harmful signals according
to embedded receptors. We extended the same control to include tropisms shown in shoots of
climbing plants and used it to analyze the emergence of shadow avoidance, 宽容, or attraction
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Artificial Life Volume 29, 数字 3
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乙. Del Dottore and B. Mazzolai
Computation in Plants
数字 2. (A) Example of a plant-inspired robot to imitate plant organ behaviors (Sadeghi et al., 2016). The robotic
agent in the figure has three soft springs arranged in a shaft to induce linear actuation. By differentially commanding the
motors of the springs, the robotic agent bends. (乙) Robot sensor scheme and vector field generation. The tip embeds
humidity, temperature, tactile sensors, and an accelerometer. A specific tropism (希) results from a summation of the
vector field generated from each stimulus (u j
我 , with j going over the sensors associated with tropism i). The preferential
direction to take is obtained by aggregating all the tropisms. (C) Example of the robotic agent response when stimulated
with a high-temperature source (a soldering iron). (d) Example of the robotic agent response when stimulated with a
source of humidity (wetted soil). (e–f) A sequence of the robotic agent’s response to tactile stimulation. After contact
with the finger, the robot moves away. If no other signals are present, gravitropism prevails, guiding the robotic agent
to orient itself toward the vertical.
behaviors (Del Dottore et al., 2021). Organs in plants have different specializations and thus can
offer alternative and complementary sets of behavioral responses to aid exploration strategies in
different scenarios. 此外, different incoming signals undergo different internal processes in
植物 (Ponce et al., 2017; Shkolnik & Fromm, 2016). This suggests that for a better understanding
and definition of plant control architecture, we might need to lower the level of abstraction and
provide signal-specific elaboration processes for each possible behavior. Gravity is perceived by
plant roots through statolitic cells located in the root cap (数字 3). These cells have components,
数字 3. An example of information processing in plants. Root apical regions perceive gravity by amyloplast sedimenta-
tion in statolitic cells. The information transduces at the molecular level, affecting protein (PIN3, PIN7) distribution and
结盟. Auxin is transported differentially on tissues through PIN distribution. Tissue-specific responses to auxin
level elongate the motor cells with an overall curvature toward gravity in plant roots.
Artificial Life Volume 29, 数字 3
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乙. Del Dottore and B. Mazzolai
Computation in Plants
called amyloplasts, that move following the gravity vector. When their inclination with respect to
gravity goes over a minimum threshold (Abas et al., 2006; Swarup et al., 2005), it triggers the re-
orientation of auxin transporters at the flanks of the roots so that auxin is differentially distributed
(Furutani et al., 2020). Different auxin concentrations activate tissue-specific responses (Di Mambro
等人。, 2017) to reach a macroscopic behavior called gravitropism (锗 & 陈, 2019). Differential dis-
tribution of auxin has also been observed in thigmotropic curvature responses (touch responses).
实际上, mechanostimulation induces a polar orientation of cell auxin transporters, accelerating the
active bending response of a root encountering an obstacle (李等人。, 2020). Mechanosensing may
also be implicated in amyloplast sedimentation perception, triggering calcium-mediated gravity re-
sponse pathways (Perbal & Driss-Ecole, 2003; K. Takahashi et al., 2021). 相比之下, no auxin
gradient distribution has been observed in hydrotropism (Shkolnik et al., 2016). 这, 然而,
does not rule out the involvement of auxin in water signal transduction and transmission. 实际上,
genes in Arabidopsis plants involved in auxin signal transduction are affected during hydrotropic
responses (Miao et al., 2018). Amyloplast degradation is also observed in roots under water stress
(氮. Takahashi et al., 2003; Ponce et al., 2008), suggesting the need to suppress gravity signals by
hydrotropism. Amyloplast degradation is also induced in high soil salinity conditions (孙等人。,
2008), reducing gravity perception and prioritizing escaping from dangerous conditions. Chemo-
tropism (Ferrieri et al., 2017), 另一方面, remains largely under investigated. Each nutri-
耳鼻喉科, metal, or salt stimulates different responses (Galvan-Ampudia & Testerink, 2011; 霍奇, 2004;
J. F. Ma, 2005; 孙等人。, 2008), suggesting that chemotropism is made up of multiple subbehav-
iors. Of these subbehaviors, foraging strategies are particularly interesting (Cahill & McNickle,
2011). The Michaelis–Menten kinetics is known to drive nutrient uptake, which adapts to the
plant nutrient content (爱泼斯坦, 1972; Marschner, 2011). From this principle, in a previous article
we proposed a feedback control to adjust nutrient-specific weights and, 最后, 缺点-
tribution of chemotropic behaviors in the overall collaborative behavior observed in a swarm of
rootlike robotic agents addressing the survival of the plant individual (Del Dottore, Mondini, 等人。,
2018). Each agent acted independently from the other agents, with no overall world knowledge or
consciousness of other agents’ actions but relying only on local perception, shared resources on the
basis of each agent’s needs, and local nutrient memory. The swarm reached a balance of nutrients at
the level of the plant maximizing its wellness, without needing a central coordinator to set a global
goal or function or to act as a task allocator. Crosstalk exists between tropisms (Massa & Gilroy,
2003; H. Takahashi & 斯科特, 1991; 氮. Takahashi et al., 2002), implying the co-contribution of dif-
ferent signals (xi with i = 1…m sensory nodes) in the achievement of a unified growth response
(φl) and justifying the use of a behavior coordination module (例如, cooperative summation), 哪个
might be guided by the same messenger (例如, the growth hormone auxin; Retzer et al., 2014) or by
alternative biosynthetic pathways converging into analogous responses (Muthert et al., 2020). 这
single perceptron model (方程 1) needs to mutate into a multilayer perceptron (芦苇 & 分数,
1999; Scheres & Van Der Putten, 2017) (数字 4), in which the first layer of hidden nodes represents
all the possible behaviors obtained with some activation function (φj, 例如, sine law in gravitropism;
Mullen et al., 2000) for the aggregated value of weighted (wij) input signals (希) that go above the
activation threshold (bj). To consider the possible mutual suppression of behaviors, as in the case
of hydrotropism over gravitropism, a fully connected network with all the external signals entering
the behaviors in the motor scheme architecture can be built. The wij can then be set to activate or
deactivate specific connections and define the signal summation or inhibition (negative weight). 在
同一时间, the mutualistic effect of behaviors in plants that express phenotyping during their
lifetimes suggests using an adaptive module for short- 和长期记忆, by which weights
of individual behaviors are adjusted to reflect adaptation over time (数字 4). The interrelation
among tropisms likely significantly influences circumnutation movements in plant roots (Mullen
等人。, 1998; Stolarz, 2009). In agreement with the multilayer perceptron-like model proposed in
数字 4, we previously adopted continuous-time recurrent neural networks (a class of multilayer
perceptron) to describe tissue-dependent responses to auxin fluxes and investigate the role of cir-
cumnutations and their emergence in roots (Loshchilov et al., 2021). This study highlighted the
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Computation in Plants
数字 4. A multilayer perceptron as a model for representing plant control. The coordination among multiple stimuli-
associated behaviors produces the final growth response toward an attractor in space.
mutual role of gravitropism and thigmotropism in the occurrence of oscillatory movement patterns
and the possible role of these oscillations in exploring resources (chemotropism). 然而, 如何
precisely behaviors are set, which functions model adaptation best, and what the signaling path-
ways are for each tropism and, 最后, the signal processing to apply are just some of the
open issues requiring a better understanding of molecular changes during plant cell development
(Muthert et al., 2020). The multilayer perceptron architecture is a general model that can be de-
tailed for each behavior node and on the adaptive module, on the basis of new knowledge about
plant functions or according to the needs of a specific application.
4 Extended Perceptron
Plants create networks with other plants and organisms. They establish underground networks made
up of roots and fungi in the soil, called mycorrhizal networks (Simard et al., 2012). This intense inter-
connection facilitates the survival of many species by improving the colonization of large areas,
觅食, and activating defense mechanisms (Beiler et al., 2010; Selosse et al., 2006). They likely
function as an infrastructure for exchanging nutrients to recover from abiotic stress (Van’t Padje,
Bonfante, 等人。, 2021; Van’t Padje, Werner, 等人。, 2021) and propagate warning signals to prevent
biotic attacks at far distances (Alaux et al., 2020; Babikova et al., 2013). Mycorrhizae can medi-
ate competing behaviors among enemy plants and promote cooperative behaviors among relatives
(Gorzelak et al., 2015; Kiers & Heijden, 2006). This underground social network is known as the
Wood Wide Web (Beiler et al., 2010). Some attempts to improve its understanding have been made
through mechanistic, statistical network models and market or complex adaptive system theories
(Simard et al., 2012; Van’t Padje, Werner, 等人。, 2021). The Wood Wide Web is, 实际上, a fascinating
example of a fault-tolerant, complex adaptive system in nature (Messier et al., 2013; Simard, 2009;
Van Dorp et al., 2020). From a phytocentric point of view, plants are nodes and fungi are links in
this network. The number of nodes (the plants) in the network is dynamic. Each node is a dynamic
multilayer perceptron-like model expressing the phenotype of an individual plant. Each node has
a dynamic number of sensory inputs, varying with symbiotic connections that work as a highway
to transfer traces sent by distant nodes. Through the network, the perception of a single plant is
augmented, with the information processing, enhanced at the community level, having dynamic
connectivity patterns. The environment remains central in the dynamics of this complex system.
Computation is achieved by opening and closing symbiotic connections, source-sink transfer mech-
万物有灵论, and possibly other active transport and regulatory mechanisms that define the information
flow and its distribution path over the network (Ezawa & Saito, 2018; Jin et al., 2012). The exact
rules of message exchange, transport mechanisms, signal molecules, and regulatory pathways of
沟通, especially concerning biotic alarm signals (约翰逊 & 吉尔伯特, 2015), are still not
清除. 尽管如此, interest in understanding the plant–fungus relationship is rapidly growing due
to its crucial importance in regulating the carbon cycle and limiting the effects of climate change. 一个
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Computation in Plants
engineering-driven approach can provide alternative perspectives and tools to benefit investigations.
Our computational model can be extended to describe mycorrhizal dynamics, building up process-
ing on its nodes and connections upon new knowledge about how the information is computed
and combined over the network. The extended perceptron would reflect an evolutionary state of
the forest over time, displaying intelligent behavior in the form of phenotypic adaptation to pertur-
bations in the environment. This tool would help predict alterations in an ecosystem and could be
used to define forest management strategies.
5 结论
We have provided a perspective on plant behaviors and how they can become the basis of com-
putational models. These models can be exploited to develop plant-inspired control strategies and
algorithms while favoring biological investigations for a better understanding of plant behaviors.
Research on plant-inspired engineering is still underexplored and suffers from a noncomprehensive
view of plant functioning. Owing to complex interconnections among biosynthetic pathways, 那里
is still no complete understanding of plant activation functions in response to environmental sig-
nals. 尽管如此, in this article we have reported some mechanisms of environmentally mediated
responses in plants, allowing us to define an abstraction for a generalizable plant like multilayer per-
ceptron that also complies with the motor scheme of behavior-based architectures. We adopted this
approach to imitate plants’ behavioral strategies for monitoring (Sadeghi et al., 2016) or foraging
(Del Dottore, Mondini, 等人。, 2018; Del Dottore et al., 2021) with adaptive adjustment of network
重量 (Del Dottore, Mondini, 等人。, 2018). This control strategy could be exploited in explorative
autonomous robots, 例如, to locate resources that dissolve in gradients into the environment,
such as water, salts, or some metals in soil. 此外, we have adopted continuous-time recurrent
neural networks to investigate the role of circumnutations and the reasons for their emergence in
plant roots (Loshchilov et al., 2021). Plants have already been investigated by analogy with multi-
layer perceptrons (Scheres & Van Der Putten, 2017), focusing on how such information-processing
structure can be replicated at the molecular level. Here we corroborate this idea and suggest that this
computational structure can help in the study of adaptive behavior dynamics across multiple imple-
mentation scales. Multilayer perceptron models convey computations carried out at different levels
in the hierarchical structure of plants and could be extended to dynamic computational architec-
tures to capture communication rules behind cooperative and competitive behaviors implemented
in communities of plants with mycorrhizal networks. Despite vast and growing research communi-
ties on plant biology, ecology, and mycorrhiza, 迄今为止, such a specific biological model has rarely
been explored. Further investigations into plant and fungi physiology and molecular biology could
unravel essential functional rules that would consolidate and refine our proposed plant control ar-
chitecture by answering some key questions: How are the weights of single behaviors adjusted?
Which behaviors involve crosstalk? and What are the specific signal transduction and transmission
功能? These questions are nested into a hierarchical organization of plants displaying behaviors
at the level of organs, 有机体, and communities (Ferlian et al., 2018), appealing to the engineering
世界. We believe that this approach could facilitate new solutions for autonomous environment
勘探, showing better adaptation and compliance in mutable conditions. Our approach could
also promote engineering-driven knowledge by raising more awareness for more sustainable man-
agement of resources for agricultural applications, forest management, and ecosystem restoration.
With this contribution, we hope to stimulate a cross-disciplinary discussion, which is fundamental
to boosting the development of novel technologies and stimulating overall scientific progress.
致谢
This work has received funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation program (grant 101003304) (I-WOOD).
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