Adaptive behaviour and learning in slime moulds: the role of oscillations

The main sources of molecular organization in the cell. Atlas of self-organized and self-regulated dynamic biostructures

One of the most important goals of contemporary biology is to understand the principles of the molecular order underlying the complex dynamic architecture of cells. Here, we present an overview of the main driving forces involved in the cellular molecular complexity and in the emergent functional dynamic structures, spanning from the most basic molecular organization levels to the complex emergent integrative systemic behaviors. First, we address the molecular information processing which is essential in many complex fundamental mechanisms such as the epigenetic memory, alternative splicing, regulation of transcriptional system, and the adequate self-regulatory adaptation to the extracellular environment. Next, we approach the biochemical self-organization, which is central to understand the emergency of metabolic rhythms, circadian oscillations, and spatial traveling waves. Such a complex behavior is also fundamental to understand the temporal compartmentalization of the cellular metabolism and the dynamic regulation of many physiological activities. Numerous examples of biochemical self-organization are considered here, which show that practically all the main physiological processes in the cell exhibit this type of dynamic molecular organization. Finally, we focus on the biochemical self-assembly which, at a primary level of organization, is a basic but important mechanism for the order in the cell allowing biomolecules in a disorganized state to form complex aggregates necessary for a plethora of essential structures and physiological functions. In total, more than 500 references have been compiled in this review. Due to these main sources of order, systemic functional structures emerge in the cell, driving the metabolic functionality towards the biological complexity.

Brain and cognition: The need for a broader biological perspective to overcome old biases

Even with accumulating knowledge, no consensus regarding the understanding of intelligence or cognition exists, and the universal brain bases for these functions remain unclear. Traditionally, our understanding of cognition is based on self-evident principles that appear indisputable when looking only at our species; however, this can distance us from understanding its essence (anthropocentrism, corticocentrism, intellectocentrism, neurocentrism, and idea of orthogenesis of brain evolution). Herein, we use several examples from biology to demonstrate the usefulness of comparative ways of thinking in relativizing these biases. We discuss the relationship between the number of neurons and cognition and draw attention to the highly developed cognitive performance of animals with small brains, to some "tricks" of evolution, to how animals cope with small hardware, to some animals with high-quality brains with an alternative architecture to vertebrates, and to surprising basal cognitive skills in aneural, unicellular organisms. Cognition can be supplemented by the idea of a multicellular organism as a continuum, with many levels of cognitive function, including the possible basal learning in single cells.

Addressing multiscale microbial challenges using the Mesolens

We provide a brief review of the development and application of the Mesolens and its impact on microbiology. Microbial specimens such as infected tissue samples, colonies surfaces, and biofilms are routinely collected at the mesoscale. This means that they are relatively large multimillimetre-sized samples which contain microscopic detail that must be observed to answer important questions across various sectors. The Mesolens presents the ideal imaging method to study these specimens as no other optical microscope can thanks to its unique combination of low magnification and high numerical aperture providing large field-of-view, high-resolution imaging. We demonstrate the current applications of the Mesolens to microbial imaging and go on to outline the huge potential of the Mesolens to impact other key areas of microbiology.

Assessment and the regulation of adaptive phenotypic plasticity

Organisms can react to environmental variation by altering their phenotype, and such phenotypic plasticity is often adaptive. This plasticity contributes to the diversity of phenotypes across the tree of life. Generally, the production of these phenotypes must be preceded by assessment, where the individual acquires information about its environment and phenotype relative to that environment, and then determines if and how to respond with an alternative phenotype. The role of assessment in adaptive plasticity is, therefore, crucial. In this Review, we (1) highlight the need for explicitly considering the role of assessment in plasticity; (2) present two different models for how assessment and the facultative production of phenotypes are related; and (3) describe an overarching framework for how assessment evolves. In doing so, we articulate avenues of future work and suggest that explicitly considering the role of assessment in the evolution of plasticity is key to explaining how and when plasticity occurs. Moreover, we emphasize the need to understand the role of assessment in adaptive versus maladaptive plasticity, which is an issue that will become increasingly important in a rapidly changing world.

Walk this way: modeling foraging ant dynamics in multiple food source environments

Foraging for resources is an essential process for the daily life of an ant colony. What makes this process so fascinating is the self-organization of ants into trails using chemical pheromone in the absence of direct communication. Here we present a stochastic lattice model that captures essential features of foraging ant dynamics inspired by recent agent-based models while forgoing more detailed interactions that may not be essential to trail formation. Nevertheless, our model's results coincide with those presented in more sophisticated theoretical models and experiments. Furthermore, it captures the phenomenon of multiple trail formation in environments with multiple food sources. This latter phenomenon is not described well by other more detailed models. We complement the stochastic lattice model by describing a macroscopic PDE which captures the basic structure of lattice model. The PDE provides a continuum framework for the first-principle interactions described in the stochastic lattice model and is amenable to analysis. Linear stability analysis of this PDE facilitates a computational study of the impact various parameters impart on trail formation. We also highlight universal features of the modeling framework that may allow this simple formation to be used to study complex systems beyond ants.

On Transducing Properties of Kombucha-Proteinoid Complexes

We investigate the information processing capacities of kombucha-proteinoid proto-brains, focusing on the transducing properties through accommodation spiking, tonic bursting spiking, and optical and acoustic stimulation. We explore self-organization, adaptability, and emergent phenomena in this unconventional proto-architecture. By constructing kombucha-proteinoid networks exposed to diverse audio stimuli, we analyze nonlinear dynamics using time series analysis. Assessing information representation in the presence of extreme noise, we examine the system's resilience. Our results illustrate intricate pathways resulting from the interplay between the synthetic biological substrate and bio-inspired stimulation. The kombucha-proteinoid proto-brains consistently map complex stimuli to distinct activation levels, showcasing their adaptability and potential for information processing without the need for external shaping circuits.

Electrical integrity and week-long oscillation in fungal mycelia

The electrical potential of the mycelia of a cord-forming wood decay fungus, Pholiota brunnescens, was monitored for over 100 days on a plain agar plate during the colonization onto a wood bait. Causality analyses of the electrical potential at different locations of the mycelium revealed a clear and stable causal relationship with the directional flow of the electrical potential from the hyphae at the bait location to other parts of the mycelium. However, this causality disappeared after 60 days of incubation, coinciding with the onset of slow electrical oscillation at the bait location, which occurred over one week per oscillation cycle. We speculated that the hyphae that initially colonized the bait may act as a temporary activity center, which generates electrical signals to other parts of the mycelium, thereby facilitating the colonization of the entire mycelial body to the bait. The week-long electrical oscillation represents the longest oscillation period ever recorded in fungi and warrants further investigation to elucidate its function and stability in response to environmental stimuli.

Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks

We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an activation signal and an error signal whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multimechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multimechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks (Anisetti, Scellier, et al., 2023) also falls under the rubric of multimechanism learning.

Different Morphotypes of Physarum polycephalum as Models for Chemotaxis and Locomotion

The acellular slime mold Physarum polycephalum is a large, unicellular amoeba, which, due to its huge size, is well suited to investigate chemotaxis and cellular locomotion. The myxomycete has an astonishing behavioral repertoire and is highly responsive to changes in its environment, which map to changes in its tubular network, internal cytoplasm flow, and cytoskeleton. The behavioral repertoire includes problem-solving, decision-making, and memory. P. polycephalum's chemo- and phototaxis are especially well studied. This chapter describes how to cultivate different morphotypes of P. polycephalum (micro-, meso-, and macroplasmodia). Furthermore, the setup of a chemotaxis experiment and the acquisition and analysis of chemotaxis data is described.

Thoughts from the forest floor: a review of cognition in the slime mould Physarum polycephalum

Sensing, communication, navigation, decision-making, memory and learning are key components in a standard cognitive tool-kit that enhance an animal's ability to successfully survive and reproduce. However, these tools are not only useful for, or accessible to, animals-they evolved long ago in simpler organisms using mechanisms which may be either unique or widely conserved across diverse taxa. In this article, I review the recent research that demonstrates these key cognitive abilities in the plasmodial slime mould Physarum polycephalum, which has emerged as a model for non-animal cognition. I discuss the benefits and limitations of comparisons drawn between neural and non-neural systems, and the implications of common mechanisms across wide taxonomic divisions. I conclude by discussing future avenues of research that will draw the most benefit from a closer integration of Physarum and animal cognition research.

Active oscillations in microscale navigation

Living organisms routinely navigate their surroundings in search of better conditions, more food, or to avoid predators. Typically, animals do so by integrating sensory cues from the environment with their locomotor apparatuses. For single cells or small organisms that possess motility, fundamental physical constraints imposed by their small size have led to alternative navigation strategies that are specific to the microscopic world. Intriguingly, underlying these myriad exploratory behaviours or sensory functions is the onset of periodic activity at multiple scales, such as the undulations of cilia and flagella, the vibrations of hair cells, or the oscillatory shape modes of migrating neutrophils. Here, I explore oscillatory dynamics in basal microeukaryotes and hypothesize that these active oscillations play a critical role in enhancing the fidelity of adaptive sensorimotor integration.

Foraging strategies of fungal mycelial networks: responses to quantity and distance of new resources

Fungal mycelial networks are essential for translocating and storing water, nutrients, and carbon in forest ecosystems. In particular, wood decay fungi form mycelial networks that connect various woody debris on the forest floor. Understanding their foraging strategies is crucial for complehending the role of mycelium in carbon and nutrient cycling in forests. Previous studies have shown that mycelial networks initiate migration from the original woody resource (inoculum) to a new woody resource (bait) if the latter is sufficiently large but not if it is small. However, the impact of energetic costs during foraging, such as the distance to the bait, has not been considered. In the present study, we conducted full-factorial experiments with two factors, bait size (4 and 8 cm3) and distance from the inoculum (1 and 15 cm). An inoculum wood block, colonized by the wood decay fungus Phanerochaete velutina, was placed in one corner of a bioassay dish (24 cm × 24 cm) filled with unsterilized soil. Once the mycelium grew onto the soil to a distance >15 cm from the inoculum, a sterilized new bait wood block (of either size) was placed on the soil at one of the two distances to be colonized by the mycelia from the inoculum. After 50 days of incubation, the baits were harvested, and their dried weight was measured to calculate the absolute weight loss during incubation. The inoculum wood blocks were retrieved and placed on a new soil dish to determine whether the mycelium would grow out onto the soil again. If no growth occurred within 8 days of additional incubation, we concluded that the mycelium had migrated from the inoculum to the bait. The results showed that mycelia in inocula coupled with baits positioned 1 cm away migrated to the baits more frequently than those with baits positioned 15 cm away. A structural equation model revealed that bait weight loss (energy gain) and hyphal coverage on the soil (foraging cost) significantly influenced mycelial migration decisions. These findings suggest that fungal mycelia may employ their own foraging strategies based on energetic benefits.

Biomimetic method of emergency life channel urban planning in Wuhan using slime mold networks

This study investigated a bio-inspired approach to planning optimal routes for urban hospital life channels to enable better responses to urban public security incidents. An experimental slime mold network and an origin-destination (OD) network model in which the nodes were tertiary hospitals in Wuhan were constructed. Correlation metrics of the two network models were used for network analysis and visualization. The experimental results showed that the slime mold network was better than the OD network in terms of global optimization. Furthermore, significant polarization of the influence value of urban hospital nodes resulted in a power-law distribution. This paper presents an urban planning method in which the biological mechanism of slime mold foraging is applied to construct shortest path networks in an emergency life channels. The results can be used to examine the relationship between urban roads and hospital nodes and the rational of global optimization distribution when planning the locations of new hospitals. A set of replicable and sustainable methods for conducting a biomimetic slime mold experiment to model real environments are presented. This approach provides a novel perspective for modeling emergency life channels.

Orbiting Self-Organization of Filament-Tethered Surface-Active Droplets

Dissipative chemical systems hold the potential to enable life-like behavior in synthetic matter, such as self-organization, motility, and dynamic switching between different states. Here, out-of-equilibrium self-organization is demonstrated by interconnected source and drain droplets at an air-water interface, which display dynamic behavior due to a hydrolysis reaction that generates a concentration gradient around the drain droplets. This concentration gradient interferes with the adhesion of self-assembled amphiphile filaments that grow from a source droplet. The chemical gradient sustains a unique orbiting of the drain droplet, which is proposed to be driven by the selective adhesion of the filaments to the front of the moving droplet, while filaments approaching from behind are destabilized upon contact with the hydrolysis product in the trail of the droplet. Potential applications are foreseen in the transfer of chemical signals amongst communicating droplets in rearranging networks, and the implementation of chemical reactions to drive complex positioning routines in life-like systems.

Behavioural changes in slime moulds over time

Changes in behaviour over the lifetime of single-cell organisms have primarily been investigated in response to environmental stressors. However, growing evidence suggests that unicellular organisms undergo behavioural changes throughout their lifetime independently of the external environment. Here we studied how behavioural performances across different tasks vary with age in the acellular slime mould Physarum polycephalum. We tested slime moulds aged from 1 week to 100 weeks. First, we showed that migration speed decreases with age in favourable and adverse environments. Second, we showed that decision making and learning abilities do not deteriorate with age. Third, we revealed that old slime moulds can recover temporarily their behavioural performances if they go throughout a dormant stage or if they fuse with a young congener. Last, we observed the response of slime mould facing a choice between cues released by clone mates of different age. We found that both old and young slime moulds are attracted preferentially toward cues left by young slime moulds. Although many studies have studied behaviour in unicellular organisms, few have taken the step of looking for changes in behaviour over the lifetime of individuals. This study extends our knowledge of the behavioural plasticity of single-celled organisms and establishes slime moulds as a promising model to investigate the effect of ageing on behaviour at the cellular level. This article is part of a discussion meeting issue 'Collective behaviour through time'.

From representations to servomechanisms to oscillators: my journey in the study of cognition

The study of comparative cognition bloomed in the 1970s and 1980s with a focus on representations in the heads of animals that undergird what animals can achieve. Even in action-packed domains such as navigation and spatial cognition, a focus on representations prevailed. In the 1990s, I suggested a conception of navigation in terms of navigational servomechanisms. A servomechanism can be said to aim for a goal, with deviations from the goal-directed path registering as an error. The error drives action to reduce the error in a negative-feedback loop. This loop, with the action reducing the very signal that drove action in the first place, is key to defining a servomechanism. Even though actions are crucial components of servomechanisms, my focus was on the representational component that encodes signals and evaluates errors. Recently, I modified and amplified this view in claiming that, in navigation, servomechanisms operate by modulating the performance of oscillators, endogenous units that produce periodic action. The pattern is found from bacteria travelling micrometres to sea turtles travelling thousands of kilometres. This pattern of servomechanisms working with oscillators is found in other realms of cognition and of life. I think that oscillators provide an effective way to organise an organism's own activities while servomechanisms provide an effective means to adjust to the organism's environment, including that of its own body.

Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence

Philosophical and theoretical debates on the multiple realisability of the cognitive have historically influenced discussions of the possible systems capable of instantiating complex functions like memory, learning, goal-directedness, and decision-making. These debates have had the corollary of undermining, if not altogether neglecting, the materiality and corporeality of cognition-treating material, living processes as "hardware" problems that can be abstracted out and, in principle, implemented in a variety of materials-in particular on digital computers and in the form of state-of-the-art neural networks. In sum, the matter in se has been taken not to matter for cognition. However, in this paper, we argue that the materiality of cognition-and the living, self-organizing processes that it enables-requires a more detailed assessment when understanding the nature of cognition and recreating it in the field of embodied robotics. Or, in slogan form, that the matter matters for cognitive form and function. We pull from the fields of Active Matter Physics, Soft Robotics, and Basal Cognition literature to suggest that the imbrication between material and cognitive processes is closer than standard accounts of multiple realisability suggest. In light of this, we propose upgrading the notion of multiple realisability from the standard version-what we call 1.0-to a more nuanced conception 2.0 to better reflect the recent empirical advancements, while at the same time averting many of the problems that have been raised for it. These fields are actively reshaping the terrain in which we understand materiality and how it enables, mediates, and constrains cognition. We propose that taking the materiality of our embodied, precarious nature seriously furnishes an important research avenue for the development of embodied robots that autonomously value, engage, and interact with the environment in a goal-directed manner, in response to existential needs of survival, persistence, and, ultimately, reproduction. Thus, we argue that by placing further emphasis on the soft, active, and plastic nature of the materials that constitute cognitive embodiment, we can move further in the direction of autonomous embodied robots and Artificial Intelligence.

Social complexity and brain evolution: insights from ant neuroarchitecture and genomics

Brain evolution is hypothesized to be driven by requirements to adaptively respond to environmental cues and social signals. Diverse models describe how sociality may have influenced eusocial insect-brain evolution, but specific impacts of social organization and other selective forces on brain architecture have been difficult to distinguish. Here, we evaluate predictions derived from and/or inferences made by models of social organization concerning the effects of individual and collective behavior on brain size, structure, and function using results of neuroanatomical and genomic studies. In contrast to the predictions of some models, we find that worker brains in socially complex species have great behavioral and cognitive capacity. We also find that colony size, the evolution of worker physical castes, and task specialization affect brain size and mosaicism, supporting the idea that sensory, processing and motor requirements for behavioral performance select for adaptive allometries of functionally specialized brain centers. We review available transcriptomic and comparative genomic studies seeking to elucidate the molecular pathways functionally associated with social life and the genetic changes that occurred during the evolution of social complexity. We discuss ways forward, using comparative neuroanatomy, transcriptomics, and comparative genomics, to distinguish among multiple alternative explanations for the relationship between the evolution of neural systems and social complexity.

Do plants pay attention? A possible phenomenological-empirical approach

Attention is the important ability of flexibly controlling limited cognitive resources. It ensures that organisms engage with the activities and stimuli that are relevant to their survival. Despite the cognitive capabilities of plants and their complex behavioural repertoire, the study of attention in plants has been largely neglected. In this article, we advance the hypothesis that plants are endowed with the ability of attaining attentive states. We depart from a transdisciplinary basis of philosophy, psychology, physics and plant ecophysiology to propose a framework that seeks to explain how plant attention might operate and how it could be studied empirically. In particular, the phenomenological approach seems particularly important to explain plant attention theoretically, and plant electrophysiology seems particularly suited to study it empirically. We propose the use of electrophysiological techniques as a viable way for studying it, and we revisit previous work to support our hypothesis. We conclude this essay with some remarks on future directions for the study of plant attention and its implications to botany.

Oscillators and servomechanisms in orientation and navigation, and sometimes in cognition

Navigational mechanisms have been characterized as servomechanisms. A navigational servomechanism specifies a goal state to strive for. Discrepancies between the perceived current state and the goal state specify error. Servomechanisms adjust the course of travel to reduce the error. I now add that navigational servomechanisms work with oscillators, periodic movements of effectors that drive locomotion. I illustrate this concept selectively over a vast range of scales of travel from micrometres in bacteria to thousands of kilometres in sea turtles. The servomechanisms differ in sophistication, with some interrupting forward motion occasionally or changing travel speed in kineses and others adjusting the direction of travel in taxes. I suggest that in other realms of life as well, especially in cognition, servomechanisms work with oscillators.

Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.

Intrinsically Disordered Proteins: Critical Components of the Wetware

Despite the wealth of knowledge gained about intrinsically disordered proteins (IDPs) since their discovery, there are several aspects that remain unexplored and, hence, poorly understood. A living cell is a complex adaptive system that can be described as a wetware─a metaphor used to describe the cell as a computer comprising both hardware and software and attuned to logic gates─capable of "making" decisions. In this focused Review, we discuss how IDPs, as critical components of the wetware, influence cell-fate decisions by wiring protein interaction networks to keep them minimally frustrated. Because IDPs lie between order and chaos, we explore the possibility that they can be modeled as attractors. Further, we discuss how the conformational dynamics of IDPs manifests itself as conformational noise, which can potentially amplify transcriptional noise to stochastically switch cellular phenotypes. Finally, we explore the potential role of IDPs in prebiotic evolution, in forming proteinaceous membrane-less organelles, in the origin of multicellularity, and in protein conformation-based transgenerational inheritance of acquired characteristics. Together, these ideas provide a new conceptual framework to discern how IDPs may perform critical biological functions despite their lack of structure.

Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleate syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters and if nuclei within the cell diversify into heterogeneous states. Here, we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleus cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.

Interplay of periodic dynamics and noise: Insights from a simple adaptive system

We study the dynamics of a simple adaptive system in the presence of noise and periodic damping. The system is composed by two paths connecting a source and a sink, and the dynamics is governed by equations that usually describe food search of the paradigmatic Physarum polycephalum. In this work we assume that the two paths undergo damping whose relative strength is periodically modulated in time, and we analyze the dynamics in the presence of stochastic forces simulating Gaussian noise. We identify different responses depending on the modulation frequency and on the noise amplitude. At frequencies smaller than the mean dissipation rate, the system tends to switch to the path which minimizes dissipation. Synchronous switching occurs at an optimal noise amplitude which depends on the modulation frequency. This behavior disappears at larger frequencies, where the dynamics can be described by the time-averaged equations. Here we find metastable patterns that exhibit the features of noise-induced resonances.

Identification and characterization of histones in Physarum polycephalum evidence a phylogenetic vicinity of Mycetozoans to the animal kingdom

Physarum polycephalum belongs to Mycetozoans, a phylogenetic clade apart from the animal, plant and fungus kingdoms. Histones are nuclear proteins involved in genome organization and regulation and are among the most evolutionary conserved proteins within eukaryotes. Therefore, this raises the question of their conservation in Physarum and the position of this organism within the eukaryotic phylogenic tree based on histone sequences. We carried out a comprehensive study of histones in Physarum polycephalum using genomic, transcriptomic and molecular data. Our results allowed to identify the different isoforms of the core histones H2A, H2B, H3 and H4 which exhibit strong conservation of amino acid residues previously identified as subject to post-translational modifications. Furthermore, we also identified the linker histone H1, the most divergent histone, and characterized a large number of its PTMs by mass spectrometry. We also performed an in-depth investigation of histone genes and transcript structures. Histone proteins are highly conserved in Physarum and their characterization will contribute to a better understanding of the polyphyletic Mycetozoan group. Our data reinforce that P. polycephalum is evolutionary closer to animals than plants and located at the crown of the eukaryotic tree. Our study provides new insights in the evolutionary history of Physarum and eukaryote lineages.

Living systems are smarter bots: Slime mold semiosis versus AI symbol manipulation

Although machines may be good at mimicking, they are not currently able, as organisms are, to act creatively. We offer an understanding of the emergent qualities of biological sign processing in terms of generalization, association, and encryption. We use slime mold as a model of minimal cognition and compare it to deep-learning video game bots, which some claim have evolved beyond their merely quantitative algorithms. We find that these discrete Turing machine bots are not able to make productive, yet unanticipated, "errors"-necessary for biological learning-which, based on the physicality of signs, their relatively similar shapes, and relative physical positions spatially and temporally, lead to emergent effects and make learning and evolution possible. In organisms, stochastic resonance at the local level can be leveraged for self-organization at the global level. We contrast all this to the symbolic processing of today's machine learning, whereby each logic node and memory state is discrete. Computer codes are produced by external operators, whereas biological symbols are evolved through an internal encryption process.

Reframing cognition: getting down to biological basics

The premise of this two-part theme issue is simple: the cognitive sciences should join the rest of the life sciences in how they approach the quarry within their research domain. Specifically, understanding how organisms on the lower branches of the phylogenetic tree become familiar with, value and exploit elements of an ecological niche while avoiding harm can be expected to aid understanding of how organisms that evolved later (including Homo sapiens) do the same or similar things. We call this approach basal cognition. In this introductory essay, we explain what the approach involves. Because no definition of cognition exists that reflects its biological basis, we advance a working definition that can be operationalized; introduce a behaviour-generating toolkit of capacities that comprise the function (e.g. sensing/perception, memory, valence, learning, decision making, communication), each element of which can be studied relatively independently; and identify a (necessarily incomplete) suite of common biophysical mechanisms found throughout the domains of life involved in implementing the toolkit. The articles in this collection illuminate different aspects of basal cognition across different forms of biological organization, from prokaryotes and single-celled eukaryotes-the focus of Part 1-to plants and finally to animals, without and with nervous systems, the focus of Part 2. By showcasing work in diverse, currently disconnected fields, we hope to sketch the outline of a new multidisciplinary approach for comprehending cognition, arguably the most fascinating and hard-to-fathom evolved function on this planet. Doing so has the potential to shed light on problems in a wide variety of research domains, including microbiology, immunology, zoology, biophysics, botany, developmental biology, neurobiology/science, regenerative medicine, computational biology, artificial life and synthetic bioengineering. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.

Learning in single cell organisms

The survival of all species requires appropriate behavioral responses to environmental challenges. Learning is one of the key processes to acquire information about the environment and adapt to changing and uncertain conditions. Learning has long been acknowledged in animals from invertebrates to vertebrates but remains a subject of debate in non-animal systems such a plants and single cell organisms. In this review I will attempt to answer the following question: are single cell organisms capable of learning? I will first briefly discuss the concept of learning and argue that the ability to acquire and store information through learning is pervasive and may be found in single cell organisms. Second, by focusing on habituation, the simplest form of learning, I will review a series of experiments showing that single cell organisms such as slime molds and ciliates display habituation and follow most of the criteria adopted by neuroscientists to define habituation. Then I will discuss disputed evidence suggesting that single cell organisms might also undergo more sophisticated forms of learning such as associative learning. Finally, I will stress out that the challenge for the future is less about whether or not to single cell organisms fulfill the definition of learning established from extensive studies in animal systems and more about acknowledging and understanding the range of behavioral plasticity exhibited by such fascinating organisms.