Slime mould: The fundamental mechanisms of biological cognition

Biology in the 21st century: Natural selection is cognitive selection

Natural selection has a formal definition as the natural process that results in the survival and reproductive success of individuals or groups best adjusted to their environment, leading to the perpetuation of those genetic qualities best suited to that organism's environmental niche. Within conventional Neo-Darwinism, the largest source of those variations that can be selected is presumed to be secondary to random genetic mutations. As these arise, natural selection sustains adaptive traits in the context of a 'struggle for existence'. Consequently, in the 20th century, natural selection was generally portrayed as the primary evolutionary driver. The 21st century offers a comprehensive alternative to Neo-Darwinian dogma within Cognition-Based Evolution. The substantial differences between these respective evolutionary frameworks have been most recently articulated in a revision of Crick's Central Dogma, a former centerpiece of Neo-Darwinism. The argument is now advanced that the concept of natural selection should also be comprehensively reappraised. Cognitive selection is presented as a more precise term better suited to 21st century biology. Since cognition began with life's origin, natural selection represents cognitive selection.

Hydrophobic Barriers for Directing Physarum polycephalum Propulsion and Navigation

Physarum polycephalum (P. polycephalum) is a unicellular protist with unique properties, such as learning and remembering in its cultured environment without a brain or central nervous system. The organism has been extensively used in morphology, taxis, and positive feedback dynamics studies. However, the lack of standardization of materials and substrate designs used in P. polycephalum studies has significantly limited conducting such studies, increasing the cost and time. In this study, we introduce a method to control the direction and migration of P. polycephalum by drawing hydrophobic lines and patterns. Our study succeeded in controlling the movement of P. polycephalum by setting a variety of hydrophobic designs such as complete barrier, single-slit barrier, taper barrier, dumbbell barrier, and one-side-opened rectangular barrier, suggesting the effectiveness of the hydrophobic barrier in regulating the propulsion and navigation of the organisms. Moreover, we demonstrated that utilizing such geometric constraints can reduce the experimental time required for toxicity testing based on P. polycephalum by more than 300%. Our techniques open new possibilities for studying the biophysical properties and behaviors of P. polycephalum, while also facilitating toxicity testing.

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.

Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology

A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Cognition as Morphological/Morphogenetic Embodied Computation In Vivo

Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The present-day human-centric view of cognition still prevailing in major encyclopedias has a variety of open problems. This article considers recent research about morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, free energy principle, cognition as Bayesian learning, active inference, and related topics, offering new theoretical and practical perspectives on problems inherent to the old computationalist cognitive models which were based on abstract symbol processing, and unaware of actual physical constraints and affordances of the embodiment of cognizing agents. A better understanding of cognition is centrally important for future artificial intelligence, robotics, medicine, and related fields.

Alternative model systems for cognitive variation: eusocial-insect colonies

Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search.

Concept Formation and Quantum-like Probability from Nonlocality in Cognition

Human decision-making is relevant for concept formation and cognitive illusions. Cognitive illusions can be explained by quantum probability, while the reason for introducing quantum mechanics is based on ad hoc bounded rationality (BR). Concept formation can be explained in a set-theoretic way, although such explanations have not been extended to cognitive illusions. We naturally expand the idea of BR to incomplete BR and introduce the key notion of nonlocality in cognition without any attempts on quantum theory. We define incomplete bounded rationality and nonlocality as a binary relation, construct a lattice from the relation by using a rough-set technique, and define probability in concept formation. By using probability defined in concept formation, we describe various cognitive illusions, such as the guppy effect, conjunction fallacy, order effect, and so on. It implies that cognitive illusions can be explained by changes in the probability space relevant to concept formation.

The Incubascope: a simple, compact and large field of view microscope for long-term imaging inside an incubator

Optical imaging has rapidly evolved in the last decades. Sophisticated microscopes allowing optical sectioning for three-dimensional imaging or sub-diffraction resolution are available. Due to price and maintenance issues, these microscopes are often shared between users in facilities. Consequently, long-term access is often prohibited and does not allow to monitor slowly evolving biological systems or to validate new models like organoids. Preliminary coarse long-term data that do not require acquisition of terabytes of high-resolution images are important as a first step. By contrast with expensive all-in-one commercialized stations, standard microscopes equipped with incubator stages offer a more cost-effective solution despite imperfect long-run atmosphere and temperature control. Here, we present the Incubascope, a custom-made compact microscope that fits into a table-top incubator. It is cheap and simple to implement, user-friendly and yet provides high imaging performances. The system has a field of view of 5.5 × 8 mm², a 3 μm resolution, a 10 frames per second acquisition rate, and is controlled with a Python-based graphical interface. We exemplify the capabilities of the Incubascope on biological applications such as the hatching of Artemia salina eggs, the growth of the slime mould Physarum polycephalum and of encapsulated spheroids of mammalian cells.

Studying Protista WBR and Repair Using Physarum polycephalum

Physarum polycephalum is a protist slime mould that exhibits a high degree of responsiveness to its environment through a complex network of tubes and cytoskeletal components that coordinate behavior across its unicellular, multinucleated body. Physarum has been used to study decision making, problem solving, and mechanosensation in aneural biological systems. The robust generative and repair capacities of Physarum also enable the study of whole-body regeneration within a relatively simple model system. Here we describe methods for growing, imaging, quantifying, and sampling Physarum that are adapted for investigating regeneration and repair.

Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

Genes and knowledge: Response to Baverstock, K. the gene an appraisal. https://doi.org/10.1016/j.pbiomolbio.2021.04.005

This response aims to expand on some of the issues raised by Keith Baverstock's The Gene: An Appraisal, especially on the evolution and nature of knowledge in living things. In contrast to the simple associationism envisaged in "genetic information", it emphasises the dynamic complexity and changeability of most natural environments, and, therefore, predictability based on underlying statistical structures. That seems to be the basis of the "cognitive" functions increasingly being reported about cellular, as well as more evolved, functions, and of the autonomous agency of organisms thriving creatively in complex environments.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Mechanosensation Mediates Long-Range Spatial Decision-Making in an Aneural Organism

The unicellular protist Physarum polycephalum is an important emerging model for understanding how aneural organisms process information toward adaptive behavior. Here, it is revealed that Physarum can use mechanosensation to reliably make decisions about distant objects in its environment, preferentially growing in the direction of heavier, substrate-deforming, but chemically inert masses. This long-range sensing is abolished by gentle rhythmic mechanical disruption, changing substrate stiffness, or the addition of an inhibitor of mechanosensitive transient receptor potential channels. Additionally, it is demonstrated that Physarum does not respond to the absolute magnitude of strain. Computational modeling reveales that Physarum may perform this calculation by sensing the fraction of its perimeter that is distorted above a threshold substrate strain-a fundamentally novel method of mechanosensation. Using its body as both a distributed sensor array and computational substrate, this aneural organism leverages its unique morphology to make long-range decisions. Together, these data identify a surprising behavioral preference relying on biomechanical features and quantitatively characterize how the Physarum exploits physics to adaptively regulate its growth and shape.

Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity

Improvement of mobility, especially environment-friendly green mobility, is challenging in existing megacities due to road network complexity and space constraints. Endorsing the bicycle lane network (BLN) in congested megacities is a promising option to foster green mobility. This research presents a novel bioinspired network design method that considers various constraints and preferences related to the megacity for designing an optimal BLN. The proposed method is inspired by natural Physarum polycephalum, a brainless, multi-headed single-celled organism, which is capable of developing a reticulated network of complex foraging behaviors in pursuit of food. The mathematical model of Physarum foraging behavior is adapted to maneuver various BLN constraints in megacity contexts in designing the optimal BLN. The Physarum-inspired BLN method is applied to two case studies on the megacity Dhaka for designing BLNs: the first one covers congested central city area, and the second one covers a broader area that includes major locations of the city. The obtained BLNs were evaluated comparing their available routes between different locations with the existing vehicle routes of the city in terms of distance and required travel times in different time periods, and the BLN routes were found to be suitable alternatives for avoiding congested main roads. The expected travel time using BLNs is shorter than other transport (e.g., car and public bus); additionally, at glance, the average travel speed on BLNs is almost double that of public buses in peak hours. Finally, the designed BLNs are promising for environment-friendly and healthy mobility.

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.

The requirement of cellularity for abiogenesis

The history of modern biochemistry started with the cellular theory of life. By putting aside the holistic protoplasmic theory, scientists of the XX century were able to advance the functional classification of cellular components significantly. The cell became the unit of the living. Current theories on the abiogenesis of life must account for a moment in evolution (chemical or biological) when this was not the case. Investigating the role of compartments and membranes along chemical and biotic evolution can lead a more generalised idea of living organisms that is fundamental to advance our efforts in astrobiology, origin of life and artificial life studies. Furthermore, it may provide insights in unexplained evolutionary features such as the lipid divide between Archaea and Eubacteria. By surveying our current understanding of the involvement of compartments in abiogenesis and evolution, the idea of cells as atomistic units of a general theory of biology will be discussed. The aim is not to undermine the validity of the cellular theory of life, but rather to elucidate possible biases with regards to cellularity and the origin of life. An open discussion in these regards could show the inherent limitations of non-cellular compartmentalization that may lead to the necessity of cellular structures to support complex life.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

Nervous systems' computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states-in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.

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

The slime mould Physarum polycephalum, an aneural organism, uses information from previous experiences to adjust its behaviour, but the mechanisms by which this is accomplished remain unknown. This article examines the possible role of oscillations in learning and memory in slime moulds. Slime moulds share surprising similarities with the network of synaptic connections in animal brains. First, their topology derives from a network of interconnected, vein-like tubes in which signalling molecules are transported. Second, network motility, which generates slime mould behaviour, is driven by distinct oscillations that organize into spatio-temporal wave patterns. Likewise, neural activity in the brain is organized in a variety of oscillations characterized by different frequencies. Interestingly, the oscillating networks of slime moulds are not precursors of nervous systems but, rather, an alternative architecture. Here, we argue that comparable information-processing operations can be realized on different architectures sharing similar oscillatory properties. After describing learning abilities and oscillatory activities of P. polycephalum, we explore the relation between network oscillations and learning, and evaluate the organism's global architecture with respect to information-processing potential. We hypothesize that, as in the brain, modulation of spontaneous oscillations may sustain learning in slime mould. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.

Slime molds response to carbon nanotubes exposure: from internalization to behavior

Carbon nanotubes (CNTs) offer attractive opportunities due to their physical, electrical, mechanical, optical, and thermal properties. They are used in a wide range of applications and are found in numerous consumer products. On the downside, their increasing presence in the environment poses potential threats to living organisms and ecosystems. The aim of this study was to evaluate the toxicity of double-walled carbon nanotubes (DWCNTs) on a new model system: the acellular slime mold Physarum polycephalum. Despite its ecological significance, its simplicity of organization, and its behavioral complexity, exposure of such organisms to nanoparticles has been poorly investigated. Slime molds were exposed to DWCNTs using three routes of exposure (topical, food, environment). We first demonstrated that DWCNTs were rapidly internalized by slime molds especially when DWCNTs were mixed with the food or spread out in the environment. Secondly, we showed that a 6-week exposure to DWCNTs did not lead to bioaccumulation nor did it lead to persistence in the slime molds when they entered a resting stage. Thirdly, we revealed that 2 days following exposure, DWCNTs were almost entirely excreted from the slime molds. Lastly, we uncovered that DWCNTs exposure altered the migration speed, the pseudopods formation, and the expansion rate of the slime molds. Our results extend our current knowledge of CNTs cytotoxicity and introduce P. polycephalum as an ideal organism for nanotoxicology.

A universal model describing the structure and functions of living systems

Can Life be explained based on the fundamental Laws of Nature? This question is central in Science since its answer could unify Physics and Biology and open new routes for Medicine. The present study introduces a clear and well-documented hypothesis addressing the unified description of all living systems. The proposed universal model is based on two established characteristics of Life. First, the concept of Functional Self-similarity (FSS) is introduced. As shown by several authors, all living systems can be classified in a multi-level hierarchy of increasing complexity. Systems in all hierarchical levels are characterized by a small set of the same attributes defining Life. This observation implies the existence of an elementary living system (i.e., a quantum of Life) having all the necessary functionalities of living systems. Secondly, the non-equilibrium nature of living systems implies that they should be able to process information since such a function is required for reducing entropy. Therefore, all living systems necessarily perform logical operations similar to electronic circuits. This conclusion, which is based on the requirement to overcome the constraints of the Second Law of Thermodynamics, indicates a close correspondence between living systems and information processing machines, namely computers. Consequently, important theoretical principles and concepts regarding computer design may also apply in the study of living systems. The above considerations lead to the Hypothesis of a Universal Architecture (UAH).

Hyphal and mycelial consciousness: the concept of the fungal mind

Like other cells, fungal hyphae show exquisite sensitivity to their environment. This reactiveness is demonstrated at many levels, from changes in the form of the hypha resulting from alterations in patterns of exocytosis, to membrane excitation, and mechanisms of wound repair. Growing hyphae detect ridges on surfaces and respond to restrictions in their physical space. These are expressions of cellular consciousness. Fungal mycelia show decision-making and alter their developmental patterns in response to interactions with other organisms. Mycelia may even be capable of spatial recognition and learning coupled with a facility for short-term memory. Now is a fruitful time to recognize the study of fungal ethology as a distinctive discipline within mycology.

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.

Substrate and cell fusion influence on slime mold network dynamics

The acellular slime mold Physarum polycephalum provides an excellent model to study network formation, as its network is remodelled constantly in response to mass gain/loss and environmental conditions. How slime molds networks are built and fuse to allow for efficient exploration and adaptation to environmental conditions is still not fully understood. Here, we characterize the network organization of slime molds exploring homogeneous neutral, nutritive and adverse environments. We developed a fully automated image analysis method to extract the network topology and followed the slime molds before and after fusion. Our results show that: (1) slime molds build sparse networks with thin veins in a neutral environment and more compact networks with thicker veins in a nutritive or adverse environment; (2) slime molds construct long, efficient and resilient networks in neutral and adverse environments, whereas in nutritive environments, they build shorter and more centralized networks; and (3) slime molds fuse rapidly and establish multiple connections with their clone-mates in a neutral environment, whereas they display a late fusion with fewer connections in an adverse environment. Our study demonstrates that slime mold networks evolve continuously via pruning and reinforcement, adapting to different environmental conditions.

Science-Driven Societal Transformation, Part III: Design

Climate change, biodiversity loss, and other major social and environmental problems pose severe risks. Progress has been inadequate and scientists, global policy experts, and the general public increasingly conclude that transformational change is needed across all sectors of society in order to improve and maintain social and ecological wellbeing. At least two paths to transformation are conceivable: (1) reform of and innovation within existing societal systems (e.g., economic, legal, and governance systems); and (2) the de novo development of and migration to new and improved societal systems. This paper is the final in a three-part series of concept papers that together outline a novel science-driven research and development program aimed at the second path. It summarizes literature to build a narrative on the topic of de novo design of societal systems. The purpose is to raise issues, suggest design possibilities, and highlight directions and questions that could be explored in the context of this or any R&D program aimed at new system design. This paper does not present original research, but rather provides a synthesis of selected ideas from the literature. Following other papers in the series, a society is viewed as a superorganism and its societal systems as a cognitive architecture. Accordingly, a central goal of design is to improve the collective cognitive capacity of a society, rendering it more capable of achieving and sustainably maintaining vitality. Topics of attention, communication, self-identity, power, and influence are discussed in relation to societal cognition and system design. A prototypical societal system is described, and some design considerations are highlighted.

Life, death, and self: Fundamental questions of primitive cognition viewed through the lens of body plasticity and synthetic organisms

Central to the study of cognition is being able to specify the Subject that is making decisions and owning memories and preferences. However, all real cognitive agents are made of parts (such as brains made of cells). The integration of many active subunits into a coherent Self appearing at a larger scale of organization is one of the fundamental questions of evolutionary cognitive science. Typical biological model systems, whether basal or advanced, have a static anatomical structure which obscures important aspects of the mind-body relationship. Recent advances in bioengineering now make it possible to assemble, disassemble, and recombine biological structures at the cell, organ, and whole organism levels. Regenerative biology and controlled chimerism reveal that studies of cognition in intact, "standard", evolved animal bodies are just a narrow slice of a much bigger and as-yet largely unexplored reality: the incredible plasticity of dynamic morphogenesis of biological forms that house and support diverse types of cognition. The ability to produce living organisms in novel configurations makes clear that traditional concepts, such as body, organism, genetic lineage, death, and memory are not as well-defined as commonly thought, and need considerable revision to account for the possible spectrum of living entities. Here, I review fascinating examples of experimental biology illustrating that the boundaries demarcating somatic and cognitive Selves are fluid, providing an opportunity to sharpen inquiries about how evolution exploits physical forces for multi-scale cognition. Developmental (pre-neural) bioelectricity contributes a novel perspective on how the dynamic control of growth and form of the body evolved into sophisticated cognitive capabilities. Most importantly, the development of functional biobots - synthetic living machines with behavioral capacity - provides a roadmap for greatly expanding our understanding of the origin and capacities of cognition in all of its possible material implementations, especially those that emerge de novo, with no lengthy evolutionary history of matching behavioral programs to bodyplan. Viewing fundamental questions through the lens of new, constructed living forms will have diverse impacts, not only in basic evolutionary biology and cognitive science, but also in regenerative medicine of the brain and in artificial intelligence.

Cognition in some surprising places

The most widely accepted view in the biopsychological sciences is that the cognitive functions that are diagnostic of mental operations, sentience or, more commonly, consciousness emerged fairly late in evolution, most likely in the Cambrian period. Our position dovetails with James's below - subjectivity, feeling, consciousness has a much longer evolutionary history, one that goes back to the first appearance of life. The Cellular Basis of Consciousness (CBC) model is founded on the presumption that sentience and life are coterminous; that all organisms, based on inherent cellular activities via processes that take place in excitable membranes of their cells, are sentient, have subjective experiences and feelings. These, in turn, guide the context-relevant behaviors essential for their survival in often hostile environments in constant flux. The CBC framework is reductionistic, mechanistic, and calls for bottom-up research programs into the evolutionary origin of biological consciousness.

Cellular senomic measurements in Cognition-Based Evolution

All living entities are cognitive and dependent on ambiguous information. Any assessment of that imprecision is necessarily a measuring function. Individual cells measure information to sustain self-referential homeostatic equipoise (self-identity) in juxtaposition to the external environment. The validity of that information is improved by its collective assessment. The reception of cellular information obliges thermodynamic reactions that initiate a self-reinforcing work channel. This expresses as natural cellular engineering and niche constructions which become the complex interrelated tissue ecologies of holobionts. Multicellularity is collaborative cellular information management directed towards the optimization of information quality through its collective measured assessment. Biology and its evolution can now be re-framed as the continuous process of self-referential cellular measurement in the perpetual defense of individual cellular self-identities through the collective form.

Stress signalling in acellular slime moulds and its detection by conspecifics

Unicellular organisms live in unpredictable environments. Therefore, they need to continuously assess environmental conditions and respond appropriately to survive and thrive. When subjected to rapid changes in their environment or to cellular damages, unicellular organisms such as bacteria exhibit strong physiological reactions called stress responses that can be sensed by conspecifics. The ability to detect and use stress-related cues released by conspecifics to acquire information about the environment constitutes an adaptive survival response by prompting the organism to avoid potential dangers. Here, we investigate stress signalling and its detection by conspecifics in a unicellular organism, Physarum polycephalum. Slime moulds were subjected to either biotic (i.e. nutritional) or abiotic (i.e. chemical and light) stressors or left undisturbed while they were exploring a homogeneous environment. Then, we observed the responses of slime moulds facing a choice between cues released by stressed clone mates and cues released by undisturbed ones. We found that slime moulds actively avoided environments previously explored by stressed clone mates. These results suggest that slime moulds, like bacteria or social amoeba, exhibit physiological responses to biotic and abiotic stresses that can be sensed by conspecifics. Our results establish slime moulds as a promising new model to investigate the use of social information in unicellular organisms. This article is part of the theme issue 'Signal detection theory in recognition systems: from evolving models to experimental tests'.

The brain: a concept in flux

One of the most important aspects of the scientific endeavour is the definition of specific concepts as precisely as possible. However, it is also important not to lose sight of two facts: (i) we divide the study of nature into manageable parts in order to better understand it owing to our limited cognitive capacities and (ii) definitions are inherently arbitrary and heavily influenced by cultural norms, language, the current political climate, and even personal preferences, among many other factors. As a consequence of these facts, clear-cut definitions, despite their evident importance, are oftentimes quite difficult to formulate. One of the most illustrative examples about the difficulty of articulating precise scientific definitions is trying to define the concept of a brain. Even though the current thinking about the brain is beginning to take into account a variety of organisms, a vertebrocentric bias still tends to dominate the scientific discourse about this concept. Here I will briefly explore the evolution of our 'thoughts about the brain', highlighting the difficulty of constructing a universally (or even a generally) accepted formal definition of it and using planarians as one of the earliest examples of organisms proposed to possess a 'traditional', vertebrate-style brain. I also suggest that the time is right to attempt to expand our view of what a brain is, going beyond exclusively structural and taxa-specific criteria. Thus, I propose a classification that could represent a starting point in an effort to expand our current definitions of the brain, hopefully to help initiate conversations leading to changes of perspective on how we think about this concept. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Memory inception and preservation in slime moulds: the quest for a common mechanism

Learning and memory are indisputably key features of animal success. Using information about past experiences is critical for optimal decision-making in a fluctuating environment. Those abilities are usually believed to be limited to organisms with a nervous system, precluding their existence in non-neural organisms. However, recent studies showed that the slime mould Physarum polycephalum, despite being unicellular, displays habituation, a simple form of learning. In this paper, we studied the possible substrate of both short- and long-term habituation in slime moulds. We habituated slime moulds to sodium, a known repellent, using a 6 day training and turned them into a dormant state named sclerotia. Those slime moulds were then revived and tested for habituation. We showed that information acquired during the training was preserved through the dormant stage as slime moulds still showed habituation after a one-month dormancy period. Chemical analyses indicated a continuous uptake of sodium during the process of habituation and showed that sodium was retained throughout the dormant stage. Lastly, we showed that memory inception via constrained absorption of sodium for 2 h elicited habituation. Our results suggest that slime moulds absorbed the repellent and used it as a 'circulating memory'. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Uni-cellular integration of complex spatial information in slime moulds and ciliates

Single-celled organisms show a fascinating faculty for integrating spatial information and adapting their behaviour accordingly. As such they are of potential interest for elucidating fundamental mechanisms of developmental biology. In this mini-review we highlight current research on two organisms, the true slime mould Physarum polycephalum and the ciliates Paramecium and Tetrahymena. For each of these, we present a case study how applying physical principles to explain behaviour can lead to the understanding of general principles possibly relevant to developmental biology.

Reverse-engineering growth and form in Heidelberg

The EMBO-EMBL Symposium ‘Synthetic Morphogenesis: From Gene Circuits to Tissue Architecture’ was held in Heidelberg, Germany, in March 2019, with 150 participants seeking to reverse-engineer embryogenesis, emphasizing quantitative simulation and the use of synthetic systems to test models. This highly dynamic, interdisciplinary mix of quantitative developmental genetics, bioengineering, synthetic biology and artificial life aimed to reveal how evolution exploits physical forces and genetics to implement the cell- and tissue-level decision-making required for complex morphogenesis.

A brief history of liquid computers

A substrate does not have to be solid to compute. It is possible to make a computer purely from a liquid. I demonstrate this using a variety of experimental prototypes where a liquid carries signals, actuates mechanical computing devices and hosts chemical reactions. We show hydraulic mathematical machines that compute functions based on mass transfer analogies. I discuss several prototypes of computing devices that employ fluid flows and jets. They are fluid mappers, where the fluid flow explores a geometrically constrained space to find an optimal way around, e.g. the shortest path in a maze, and fluid logic devices where fluid jet streams interact at the junctions of inlets and results of the computation are represented by fluid jets at selected outlets. Fluid mappers and fluidic logic devices compute continuously valued functions albeit discretized. There is also an opportunity to do discrete operation directly by representing information by droplets and liquid marbles (droplets coated by hydrophobic powder). There, computation is implemented at the sites, in time and space, where droplets collide one with another. The liquid computers mentioned above use liquid as signal carrier or actuator: the exact nature of the liquid is not that important. What is inside the liquid becomes crucial when reaction-diffusion liquid-phase computing devices come into play: there, the liquid hosts families of chemical species that interact with each other in a massive-parallel fashion. I shall illustrate a range of computational tasks, including computational geometry, implementable by excitation wave fronts in nonlinear active chemical medium. The overview will enable scientists and engineers to understand how vast is the variety of liquid computers and will inspire them to design their own experimental laboratory prototypes. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

A lumped parameter model of endoplasm flow in Physarum polycephalum explains migration and polarization-induced asymmetry during the onset of locomotion

The plasmodial slime mold Physarum polycephalum exhibits strong, periodic flow of cytoplasm through the veins of its network. In the special case of mesoplasmodia, a newly described starvation-induced, shape-constant morphotype, this periodic endoplasm streaming is the basis of locomotion. Furthermore, we presume that cytoplasm flow is also involved in signal transmission and signal processing. Mesoplasmodia motility resembles amoeboid locomotion. In contrast to other amoebae, however, mesoplasmodia move without extending pseudopods and retain a coherent, fan-shaped morphology throughout their steady locomotion. Attaining sizes of up to 2 mm², mesoplasmodia are also much bigger than other amoebae. We characterize this particular type of locomotion and identify patterns of movement. By using the analogy between pulsatile fluid flow through a network of elastic tubes and electrical circuits, we build a lumped model that explains observed fluid flow patterns. Essentially, the mesoplasmodium acts as a low-pass filter, permitting only low-frequency oscillations to propagate from back to front. This frequency selection serves to optimize flow and reduces power dissipation. Furthermore, we introduce a distributed element into the lumped model to explain cell polarization during the onset of chemotaxis: Biochemical cues (internal or external) lead to a local softening of the actin cortex, which in turn causes an increased flow of cytoplasm into that area and, thus, a net forward movement. We conclude that the internal actin-enclosed vein network gives the slime mold a high measure of control over fluid transport, especially by softening or hardening, which in turn leads to polarization and net movement.

Biological evolution as defense of 'self'

Although the origin of self-referential consciousness is unknown, it can be argued that the instantiation of self-reference was the commencement of the living state as phenomenal experientiality. As self-referential cognition is demonstrated by all living organisms, life can be equated with the sustenance of cellular homeostasis in the continuous defense of 'self'. It is proposed that the epicenter of 'self' is perpetually embodied within the basic cellular form in which it was instantiated. Cognition-Based Evolution argues that all of biological and evolutionary development represents the perpetual autopoietic defense of self-referential basal cellular states of homeostatic preference. The means by which these states are attained and maintained is through self-referential measurement of information and its communication. The multicellular forms, either as biofilms or holobionts, represent the cellular attempt to achieve maximum states of informational distinction and energy efficiency through individual and collective means. In this frame, consciousness, self-consciousness and intelligence can be identified as forms of collective cellular phenotype directed towards the defense of fundamental cellular self-reference.

Senomic view of the cell: Senome versus Genome

In the legacy of Thomas Henry Huxley, and his 'epigenetic' philosophy of biology, cells are proposed to represent a trinity of three memory-storing media: Senome, Epigenome, and Genome that together comprise a cell-wide informational architecture. Our current preferential focus on the Genome needs to be complemented by a similar focus on the Epigenome and a here proposed Senome, representing the sum of all the sensory experiences of the cognitive cell and its sensing apparatus. Only then will biology be in a position to embrace the whole complexity of the eukaryotic cell, understanding its true nature which allows the communicative assembly of cells in the form of sentient multicellular organisms.