Habituation in non-neural organisms: evidence from slime moulds

The "plant neurobiology" revolution

The 21st-century "plant neurobiology" movement is an amalgam of scholars interested in how "neural processes", broadly defined, lead to changes in plant behavior. Integral to the movement (now called plant behavioral biology) is a triad of historically marginalized subdisciplines, namely plant ethology, whole plant electrophysiology and plant comparative psychology, that set plant neurobiology apart from the mainstream. A central tenet held by these "triad disciplines" is that plants are exquisitely sensitive to environmental perturbations and that destructive experimental manipulations rapidly and profoundly affect plant function. Since destructive measurements have been the norm in plant physiology, much of our "textbook knowledge" concerning plant physiology is unrelated to normal plant function. As such, scientists in the triad disciplines favor a more natural and holistic approach toward understanding plant function. By examining the history, philosophy, sociology and psychology of the triad disciplines, this paper refutes in eight ways the criticism that plant neurobiology presents nothing new, and that the topics of plant neurobiology fall squarely under the purview of mainstream plant physiology. It is argued that although the triad disciplines and mainstream plant physiology share the common goal of understanding plant function, they are distinct in having their own intellectual histories and epistemologies.

Habituation as optimal filtering

Habituation, the reduction of responding to repetitive stimuli, is often conceptualized as a kind of attentional filter, amplifying salient signals at the expense of non-salient signals. No prior account has explicitly formalized filtering principles that can explain the major characteristics of habituation. In this paper, a simple probabilistic model is developed which permits analysis of the optimal filtering problem. This model exhibits the major characteristics of habituation, while also shedding light on other, relatively neglected, characteristics. These results demonstrate that habituation can be understood as a form of optimal filtering.

Long-term root electrotropism reveals habituation and hysteresis

Plant roots sense many physical and chemical cues in soil, such as gravity, humidity, light, and chemical gradients, and respond by redirecting their growth toward or away from the source of the stimulus. This process is called tropism. While gravitropism is the tendency to follow the gravitational field downwards, electrotropism is the alignment of growth with external electric fields and the induced ionic currents. Although root tropisms are at the core of their ability to explore large volumes of soil in search of water and nutrients, the molecular and physical mechanisms underlying most of them remain poorly understood. We have previously provided a quantitative characterization of root electrotropism in Arabidopsis (Arabidopsis thaliana) primary roots exposed for 5 h to weak electric fields, showing that auxin asymmetric distribution is not necessary for root electrotropism but that cytokinin biosynthesis is. Here, we extend that study showing that long-term electrotropism is characterized by a complex behavior. We describe overshoot and habituation as key traits of long-term root electrotropism in Arabidopsis and provide quantitative data about the role of past exposures in the response to electric fields (hysteresis). On the molecular side, we show that cytokinin, although necessary for root electrotropism, is not asymmetrically distributed during the bending. Overall, the data presented here represent a step forward toward a better understanding of the complexity of root behavior and provide a quantitative platform for future studies on the molecular mechanisms of electrotropism.

Perspectives on Principles of Cellular Behavior from the Biophysics of Protists

Cells are the fundamental unit of biological organization. Although it may be easy to think of them as little more than the simple building blocks of complex organisms such as animals, single cells are capable of behaviors of remarkable apparent sophistication. This is abundantly clear when considering the diversity of form and function among the microbial eukaryotes, the protists. How might we navigate this diversity in the search for general principles of cellular behavior? Here, we review cases in which the intensive study of protists from the perspective of cellular biophysics has driven insight into broad biological questions of morphogenesis, navigation and motility, and decision making. We argue that applying such approaches to questions of evolutionary cell biology presents rich, emerging opportunities. Integrating and expanding biophysical studies across protist diversity, exploiting the unique characteristics of each organism, will enrich our understanding of general underlying principles.

Relation between learning process and morphology of transport tube network in plasmodium of Physarum polycephalum

The question of whether a single-celled organism without a brain could have functions such as learning and memory has been the subject of much debate in recent years. The plasmodium of the true slime mold, Physarum polycephalum, is an ideal model organism for such a question. The plasmodium exhibits behaviors that resemble intelligence, including solving mazes, mimicking optimal rail transportation networks, predicting the weather, and solving traveling salesman problems. In addition, the plasmodium has recently been shown to have the simplest form of learning: habituation. In the experiments in which plasmodia were repeatedly allowed to cross bridges containing aversive chemicals, the habituation behavior has been confirmed. It has been shown that the habituation process involves chemicals that are stored internally. However, it is not clear how these chemicals result in change in the behavior of plasmodium during habituation learning. This study focused on the transport tube network formed in plasmodium during the above experiments. Then, the role of the network morphology in the habituation learning process was investigated. The results showed that the network morphology changes from tree to mesh type during habituation learning, and disrupting the learned network reduces habituation behavior. In addition, it was shown that the thickness oscillation frequency depends on the network morphology. The study found that in the plasmodium of P. polycephalum, a primitive organism without a brain, transport tube networks, instead of neuronal networks, play an important role in habituation learning and the resulting decision making.

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.

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'.

A Physarum-inspired approach to the Euclidean Steiner tree problem

This paper presents a novel biologically-inspired explore-and-fuse approach to solving a large array of problems. The inspiration comes from Physarum, a unicellular slime mold capable of solving the traveling salesman and Steiner tree problems. Besides exhibiting individual intelligence, Physarum can also share information with other Physarum organisms through fusion. These characteristics of Physarum imply that spawning many such organisms we can explore the problem space in parallel, each individual gathering information and forming partial solutions pertaining to a local region of the problem space. When the organisms meet, they fuse and share information, eventually forming one organism which has a global view of the problem and can apply its intelligence to find an overall solution to the problem. This approach can be seen as a "softer" method of divide and conquer. We demonstrate this novel approach, developing the Physarum Steiner Algorithm which is capable of finding feasible solutions to the Euclidean Steiner tree problem. This algorithm is of particular interest due to its resemblance to Physarum polycephalum, ability to leverage parallel processing, avoid obstacles, and operate on various shapes and topological surfaces including the rectilinear grid.

Comparison of anxiety-like and social behaviour in medaka and zebrafish

The medaka, Oryzias latipes, is rapidly growing in importance as a model in behavioural research. However, our knowledge of its behaviour is still incomplete. In this study, we analysed the performance of medaka in 3 tests for anxiety-like behaviour (open-field test, scototaxis test, and diving test) and in 3 sociability tests (shoaling test with live stimuli, octagonal mirror test, and a modified shoaling test with mirror stimulus). The behavioural response of medaka was qualitatively similar to that observed in other teleosts in the open-field test (thigmotaxis), and in 2 sociability tests, the shoaling test and in the octagonal mirror test (attraction towards the social stimulus). In the remaining tests, medaka did not show typical anxiety (i.e., avoidance of light environments and preference for swimming at the bottom of the aquarium) and social responses (attraction towards the social stimulus). As a reference, we compared the behaviour of the medaka to that of a teleost species with well-studied behaviour, the zebrafish, tested under the same conditions. This interspecies comparison indicates several quantitative and qualitative differences across all tests, providing further evidence that the medaka responds differently to the experimental settings compared to other fish models.

Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System

Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an “intelligent behavior”. In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.

Relationships Organize Information in Mind and Nature: Empirical Findings of Action–Reaction Relationships (R) in Cognitive and Material Complexity

Diverse phenomena such as feedback, interconnectedness, causality, network dynamics, and complexity are all born from Relationships. They are fundamentally important, as they are transdisciplinary and synonymous with connections, links, edges, and interconnections. The foundation of systems thinking and systems themselves consists of four universals, one of which is action–reaction Relationships. They are also foundational to the consilience of knowledge. This publication gives a formal description of and predictions of action–reaction Relationships (R) or “R-rule”. There are seven original empirical studies presented in this paper. For these seven studies, experiments for the subjects were created on software (unless otherwise noted). The experiments had the subjects complete a task and/or answer a question. The samples are generalizable to a normal distribution of the US population and they vary for each study (ranging from N = 407 to N = 34,398). With high statistical significance the studies support the predictions made by DSRP Theory regarding action–reaction Relationships including its universality as an observable phenomenon in both nature (ontological complexity) and mind (cognitive complexity); mutual dependencies on other universals (i.e., Distinctions, Systems, and Perspectives); role in structural predictions; internal structures and dynamics; efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, parallelism (between cognitive and ontological complexity), universality, and efficacy of action–reaction Relationships (R).

Cell fusion through slime mould network dynamics

Physarum polycephalum is a unicellular slime mould that has been intensely studied owing to its ability to solve mazes, find shortest paths, generate Steiner trees, share knowledge and remember past events and the implied applications to unconventional computing. The CELL model is a cellular automaton introduced in Gunji et al. (Gunji et al. 2008 J. Theor. Biol. 253, 659-667 (doi:10.1016/j.jtbi.2008.04.017)) that models Physarum's amoeboid motion, tentacle formation, maze solving and network creation. In the present paper, we extend the CELL model by spawning multiple CELLs, allowing us to understand the interactions between multiple cells and, in particular, their mobility, merge speed and cytoplasm mixing. We conclude the paper with some notes about applications of our work to modelling the rise of present-day civilization from the early nomadic humans and the spread of trends and information around the world. Our study of the interactions of this unicellular organism should further the understanding of how P. polycephalum communicates and shares information.

Perspectives Organize Information in Mind and Nature: Empirical Findings of Point-View Perspective (P) in Cognitive and Material Complexity

The importance of perspective-taking crosses disciplines and is foundational to diverse phenomena such as point-of-view, scale, mindset, theory of mind, opinion, belief, empathy, compassion, analysis, and problem solving, etc. This publication gives predictions for and a formal description of point-view Perspectives (P) or the “P-rule”. This makes the P-rule foundational to systems, systems thinking and the consilience of knowledge. It is one of four universals of the organization of information as a whole. This paper presents nine empirical studies in which subjects were asked to complete a task and/or answer a question. The samples vary for each study (ranging from N = 407 to N = 34,398) and are generalizable to a normal distribution of the US population. As was evident in Cabrera, “These studies support—with high statistical significance—the predictions made by DSRP Theory (Distinctions, Systems Relationships, Perspectives) point-view Perspectives including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (material complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., Distinctions, Systems, and Relationships); role in structural predictions; and, efficacy as a metacognitive skill”. These data suggest that point-view Perspectives (P) observably and empirically exist, and that universality, efficacy, and parallelism (between cognitive and material complexity) exist as well. The impact of this paper is that it provides empirical evidence for the phenomena of point-view perspective taking (“P-rule”) as a universal pattern/structure of systems thinking, a field in which scholarly debate is often based on invalidated opinioned frameworks; this sets the stage for theory building in the field.

Systems Organize Information in Mind and Nature: Empirical Findings of Part-Whole Systems (S) in Cognitive and Material Complexity

Part-whole Systems (S) structure is foundational to a diverse array of phenomena such as belonging and containment, networks, statistics, reductionism, holism, etc. and is extremely similar if not synonymous with sets, sorts, groups, combinations and combinatorics, clusters, etc. In Cabrera (1998), part-whole Systems (S) or “S-rule” is established as one of four universals for the organization of information and thus is foundational to systems and systems thinking as well as the consilience of knowledge. In this paper, seven empirical studies are presented in which (unless otherwise noted) subjects completed a task. Ranging from n = 407 to n = 34,398, the sample sizes vary for each study but are generalizeable to a normal distribution of the US population. With high statistical significance, the results of these studies support the predictions made by DSRP Theory regarding part-whole Systems (a.k.a., “S-rule”) including: the universality of S-rule as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); the internal structures and dynamics of S-rule; S-rule’s mutual dependencies on other universals of DSRP (Distinctions, Systems, Relationships, and Perspectives (i.e., Distinctions, Relationships, and Perspectives); the role S-rule plays in making structural predictions; and, S-rule’s efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of part-whole Systems (S).

Distinctions Organize Information in Mind and Nature: Empirical Findings of Identity–Other Distinctions (D) in Cognitive and Material Complexity

The transdisciplinary importance of distinctions is well-established as foundational to such diverse phenomena as recognition, identification, individual and social identity, marginalization, externalities, boundaries, concept formation, etc., and synonymous general ideas, such as thingness, concepts, nodes, objects, etc. Cabrera provides a formal description of and predictions for identity–other distinctions (D) or “D-rule” as one of four universals for the organization of information that is foundational to systems and systems thinking, as well as the consilience of knowledge. This paper presents seven empirical studies in which (unless otherwise noted) software was used to create an experiment for subjects to complete a task and/or answer a question. The samples varied for each study (ranging from N = 407 to N = 34,398) and were generalizable to a normal distribution of the US population. These studies support—with high statistical significance—the predictions made by DSRP theory regarding identity–other distinctions including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., relationships, systems, and perspectives); role in structural predictions; and efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of identity–other distinctions (D).

Representing Stimulus Information in an Energy Metabolism Pathway

We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.

The “Fish Tank” Experiments: Metacognitive Awareness of Distinctions, Systems, Relationships, and Perspectives (DSRP) Significantly Increases Cognitive Complexity

In the field of systems thinking, there are far too many opinioned frameworks and far too few empirical studies. This could be described as a “gap” in the research but it is more like a dearth in the research. More theory and empirical validation of theory are needed if the field and the phenomenon of systems thinking holds promise and not just popularity. This validation comes in the form of both basic (existential) and applied (efficacy) research studies. This article presents efficacy data for a set of empirical studies of DSRP Theory. According to Cabrera, Cabrera, and Midgley, DSRP Theory has equal or more empirical evidence supporting it than any existing systems theories (including frameworks, which are not theories). Four separate studies show highly statistically relevant findings for the effect of a short (less than one minute) treatment of D, S, R, and P. Subjects’ cognitive complexity and the systemic nature of their thinking increased in all four studies. These findings indicate that even a short treatment in DSRP is effective in increasing systems thinking skills. Based on these results, a longer, more in-depth treatment—such as a one hour or semester long training, such is the norm—would therefore likely garner transformative results and efficacy.

DSRP Theory: A Primer

DSRP Theory is now over 25 years old with more empirical evidence supporting it than any other systems thinking framework. Yet, it is often misunderstood and described in ways that are inaccurate. DSRP Theory describes four patterns and their underlying elements—identity (i) and other (o) for Distinctions (D), part (p) and whole (w) for Systems (S), action (a) and reaction (r) for Relationships (R), and point (ρ) and view (v) for Perspectives (P)—that are universal in both cognitive complexity (mind) and material complexity (nature). DSRP Theory provides a basis for systems thinking or cognitive complexity as well as material complexity (systems science). This paper, as a relatively short primer on the theory, provides clarity to those wanting to understand DSRP and its implications.

Neurons learn by predicting future activity

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.

Emergence of behaviour in a self-organized living matter network

What is the origin of behaviour? Although typically associated with a nervous system, simple organisms also show complex behaviours. Among them, the slime mold Physarum polycephalum, a giant single cell, is ideally suited to study emergence of behaviour. Here, we show how locomotion and morphological adaptation behaviour emerge from self-organized patterns of rhythmic contractions of the actomyosin lining of the tubes making up the network-shaped organism. We quantify the spatio-temporal contraction dynamics by decomposing experimentally recorded contraction patterns into spatial contraction modes. Notably, we find a continuous spectrum of modes, as opposed to a few dominant modes. Our data suggests that the continuous spectrum of modes allows for dynamic transitions between a plethora of specific behaviours with transitions marked by highly irregular contraction states. By mapping specific behaviours to states of active contractions, we provide the basis to understand behaviour’s complexity as a function of biomechanical dynamics.

Predictive Neuronal Adaptation as a Basis for Consciousness

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more "educated" response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

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.

Neuroecology beyond the brain: learning in Echinodermata

We propose an expansion of neuroecological comparisons to include the capabilities of brainless and non-neural organisms. We begin this enterprise by conducting a systematic search for studies on learning in echinoderms. Echinodermata are marine invertebrates comprising starfish, brittle stars, sea cucumbers, sea urchins, and sea lilies. Animals in this phylum lack any centralized brain and instead possess diffuse neural networks known as nerve nets. The learning abilities of these animals are of particular interest as, within the bilaterian clade, they are close evolutionary neighbors to chordates, a phylum whose members exhibit complex feats in learning and contain highly specialized brains. The learning capacities and limitations of echinoderms can inform the evolution of nervous systems and learning in Bilateria. We find evidence of both non-associative and associative learning (in the form of classical conditioning) in echinoderms, which was primarily focused on starfish. Additional evidence of learning is documented in brittle stars, sand dollars, and sea urchins. We then discuss the evolutionary significance of learning capabilities without a brain, the presence of embodied cognition across multiple groups, and compare the learning present in echinoderms with the impressive cognitive abilities documented in the oldest linage group within vertebrates (the major group within the phylum of chordates), fish.

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.

Correspondence insights into the role of genes in cell functionality. Comments on "The gene: An appraisal" by K. Baverstock

One of the most important goals of the post-genomic era is to understand the different sources of molecular information that regulate the functional and structural architecture of cells. In this regard, Prof. K. Baverstock underscores in his recent article "The gene: An appraisal" (Baverstock, 2021) that genes are not the leading elements in cellular functionality, inheritance and evolution. As a consequence, the theory of evolution based on the Neo-Darwinian synthesis, is inadequate for today's scientific evidence. Conversely, the author contends that life processes viewed on the basis of thermodynamics, complex system dynamics and self-organization provide a new framework for the foundations of Biology. I consider it necessary to comment on some essential aspects of this relevant work, and here I present a short overview of the main non-genetic sources of biomolecular order and complexity that underline the molecular dynamics and functionality of cells. These sources generate different processes of complexity, which encompasses from the most elementary levels of molecular activity to the emergence of systemic behaviors, and the information necessary to sustain them is not contained in the genome.

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.

Associative Conditioning Is a Robust Systemic Behavior in Unicellular Organisms: An Interspecies Comparison

The capacity to learn new efficient systemic behavior is a fundamental issue of contemporary biology. We have recently observed, in a preliminary analysis, the emergence of conditioned behavior in some individual amoebae cells. In these experiments, cells were able to acquire new migratory patterns and remember them for long periods of their cellular cycle, forgetting them later on. Here, following a similar conceptual framework of Pavlov's experiments, we have exhaustively studied the migration trajectories of more than 2000 individual cells belonging to three different species: Amoeba proteus, Metamoeba leningradensis, and Amoeba borokensis. Fundamentally, we have analyzed several relevant properties of conditioned cells, such as the intensity of the responses, the directionality persistence, the total distance traveled, the directionality ratio, the average speed, and the persistence times. We have observed that cells belonging to these three species can modify the systemic response to a specific stimulus by associative conditioning. Our main analysis shows that such new behavior is very robust and presents a similar structure of migration patterns in the three species, which was characterized by the presence of conditioning for long periods, remarkable straightness in their trajectories and strong directional persistence. Our experimental and quantitative results, compared with other studies on complex cellular responses in bacteria, protozoa, fungus-like organisms and metazoans that we discus here, allow us to conclude that cellular associative conditioning might be a widespread characteristic of unicellular organisms. This new systemic behavior could be essential to understand some key principles involved in increasing the cellular adaptive fitness to microenvironments.

Evidence of anticipatory immune and hormonal responses to predation risk in an echinoderm

Recent efforts have been devoted to the link between responses to non-physical stressors and immune states in animals, mostly using human and other vertebrate models. Despite evolutionary relevance, comparatively limited work on the appraisal of predation risk and aspects of cognitive ecology and ecoimmunology has been carried out in non-chordate animals. The present study explored the capacity of holothuroid echinoderms to display an immune response to both reactive and anticipatory predatory stressors. Experimental trials and a mix of behavioural, cellular and hormonal markers were used, with a focus on coelomocytes (analogues of mammalian leukocytes), which are the main components of the echinoderm innate immunity. Findings suggest that holothuroids can not only appraise threatening cues (i.e. scent of a predator or alarm signals from injured conspecifics) but prepare themselves immunologically, presumably to cope more efficiently with potential future injuries. The responses share features with recently defined central emotional states and wane after prolonged stress in a manner akin to habituation, which are traits that have rarely been shown in non-vertebrates, and never in echinoderms. Because echinoderms sit alongside chordates in the deuterostome clade, such findings offer unique insights into the adaptive value and evolution of stress responses in animals.

Self-Organization and Information Processing: From Basic Enzymatic Activities to Complex Adaptive Cellular Behavior

One of the main aims of current biology is to understand the origin of the molecular organization that underlies the complex dynamic architecture of cellular life. Here, we present an overview of the main sources of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors. First, we have addressed the dissipative self-organization, the principal source of molecular order in the cell. Intensive studies over the last four decades have demonstrated that self-organization is central to understand enzyme activity under cellular conditions, functional coordination between enzymatic reactions, the emergence of dissipative metabolic networks (DMN), and molecular rhythms. The second fundamental source of order is molecular information processing. Studies on effective connectivity based on transfer entropy (TE) have made possible the quantification in bits of biomolecular information flows in DMN. This information processing enables efficient self-regulatory control of metabolism. As a consequence of both main sources of order, systemic functional structures emerge in the cell; in fact, quantitative analyses with DMN have revealed that the basic units of life display a global enzymatic structure that seems to be an essential characteristic of the systemic functional metabolism. This global metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells. Here, we also discuss how the study of systemic DMN, using Artificial Intelligence and advanced tools of Statistic Mechanics, has shown the emergence of Hopfield-like dynamics characterized by exhibiting associative memory. We have recently confirmed this thesis by testing associative conditioning behavior in individual amoeba cells. In these Pavlovian-like experiments, several hundreds of cells could learn new systemic migratory behaviors and remember them over long periods relative to their cell cycle, forgetting them later. Such associative process seems to correspond to an epigenetic memory. The cellular capacity of learning new adaptive systemic behaviors represents a fundamental evolutionary mechanism for cell adaptation.

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'.

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.

Valuing what happens: a biogenic approach to valence and (potentially) affect

Valence is half of the pair of properties that constitute core affect, the foundation of emotion. But what is valence, and where is it found in the natural world? Currently, this question cannot be answered. The idea that emotion is the body's way of driving the organism to secure its survival, thriving and reproduction runs like a leitmotif from the pathfinding work of Antonio Damasio through four book-length neuroscientific accounts of emotion recently published by the field's leading practitioners. Yet while Damasio concluded 20 years ago that the homeostasis-affect linkage is rooted in unicellular life, no agreement exists about whether even non-human animals with brains experience emotions. Simple neural animals-those less brainy than bees, fruit flies and other charismatic invertebrates-are not even on the radar of contemporary affective research, to say nothing of aneural organisms. This near-sightedness has effectively denied the most productive method available for getting a grip on highly complex biological processes to a scientific domain whose importance for understanding biological decision-making cannot be underestimated. Valence arguably is the fulcrum around which the dance of life revolves. Without the ability to discriminate advantage from harm, life very quickly comes to an end. In this paper, we review the concept of valence, where it came from, the work it does in current leading theories of emotion, and some of the odd features revealed via experiment. We present a biologically grounded framework for investigating valence in any organism and sketch a preliminary pathway to a computational model. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.

Learning in Cnidaria: A systematic review

Using the database Web of Science, a systematic search for literature on learning in Cnidaria, both non-associative and associative, was conducted. Cnidaria comprise hydras, box jellies, (true) jellyfish, corals, and sea anemones, a group of animals possessing diffuse networks of nerves known as nerve nets or neural nets. Being neighbors on the animal evolutionary tree to bilaterian animals, the vast collection of (mostly) bilaterally symmetric animals with brains ranging from tiny worms to giant whales, the cognitive capacities of Cnidaria inform the evolution of nervous systems and cognition in bilateria. I failed to find literature on learning in corals and box jellies. Habituation has been amply shown in hydras, jellyfish, and sea anemones, while sensitization has been studied in detail in sea anemones, including some neurobiological details in the release of nematocysts or poisoned darts for capturing prey. One well-controlled study found evidence for classical conditioning with shock in sea anemones, in addition to two other lesser-controlled demonstrations. The relevance of associative learning in sea anemones, embodied cognition, and representationsal issues when it comes to animals without central brains is discussed.

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'.

Sport Practitioners as Sport Ecology Designers: How Ecological Dynamics Has Progressively Changed Perceptions of Skill "Acquisition" in the Sporting Habitat

Over two decades ago, Davids et al. (1994) and Handford et al. (1997) raised theoretical concerns associated with traditional, reductionist, and mechanistic perspectives of movement coordination and skill acquisition for sport scientists interested in practical applications for training designs. These seminal papers advocated an emerging consciousness grounded in an ecological approach, signaling the need for sports practitioners to appreciate the constraints-led, deeply entangled, and non-linear reciprocity between the organism (performer), task, and environment subsystems. Over two decades later, the areas of skill acquisition, practice and training design, performance analysis and preparation, and talent development in sport science have never been so vibrant in terms of theoretical modeling, knowledge generation and innovation, and technological deployment. Viewed at an ecological level of analysis, the work of sports practitioners has progressively transitioned toward the facilitation of an evolving relationship between an organism (athlete and team) and its environment (sports competition). This commentary sets out to explore how these original ideas from Davids et al. (1994) and Handford et al. (1997) have been advanced through the theoretical lens of ecological dynamics. Concurrently, we provide case study exemplars, from applied practice in high-performance sports organizations, to illustrate how these contemporary perspectives are shaping the work of sports practitioners (sport ecology designers) in practice and in performance preparation.

Defining Lyfe in the Universe: From Three Privileged Functions to Four Pillars

Motivated by the need to paint a more general picture of what life is-and could be-with respect to the rest of the phenomena of the universe, we propose a new vocabulary for astrobiological research. Lyfe is defined as any system that fulfills all four processes of the living state, namely: dissipation, autocatalysis, homeostasis, and learning. Life is defined as the instance of lyfe that we are familiar with on Earth, one that uses a specific organometallic molecular toolbox to record information about its environment and achieve dynamical order by dissipating certain planetary disequilibria. This new classification system allows the astrobiological community to more clearly define the questions that propel their research-e.g., whether they are developing a historical narrative to explain the origin of life (on Earth), or a universal narrative for the emergence of lyfe, or whether they are seeking signs of life specifically, or lyfe at large across the universe. While the concept of "life as we don't know it" is not new, the four pillars of lyfe offer a novel perspective on the living state that is indifferent to the particular components that might produce it.

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'.

Ecological memory and relocation decisions in fungal mycelial networks: responses to quantity and location of new resources

Saprotrophic cord-forming basidiomycetes, with their mycelial networks at the soil/litter interface on the forest floor, play a major role in wood decomposition and nutrient cycling/relocation. Many studies have investigated foraging behaviour of their mycelium, but there is little information on their intelligence. Here, we investigate the effects of relative size of inoculum wood and new wood resource (bait) on the decision of a mycelium to remain in, or migrate from, inoculum to bait using Phanerochaete velutina as a model. Experiments allowed mycelium to grow from an inoculum across the surface of a soil microcosm where it encountered a new wood bait. After colonisation of the bait, the original inoculum was moved to a tray of fresh soil to determine whether the fungus was still able to grow out. This also allowed us to test the mycelium's memory of growth direction. When inocula were transferred to new soil, there was regrowth from 67% of the inocula, and a threshold bait size acted as a cue for the mycelium's decision to migrate for a final time, rather than a threshold of relative size of inoculum: bait. There was greater regrowth from the side that originally faced the new bait, implying memory of growth direction.

Optogenetically induced cellular habituation in non-neuronal cells

Habituation, defined as the reversible decrement of a response during repetitive stimulation, is widely established as a form of non-associative learning. Though more commonly ascribed to neural cells and systems, habituation has also been observed in single aneural cells, although evidence is limited. Considering the generalizability of the habituation process, we tested the degree to which organism-level behavioral and single cell manifestations were similar. Human embryonic kidney (HEK) cells that overexpressed an optogenetic actuator were photostimulated to test the effect of different stimulation protocols on cell responses. Depolarization induced by the photocurrent decreased successively over the stimulation protocol and the effect was reversible upon withdrawal of the stimulus. In addition to frequency- and intensity-dependent effects, the history of stimulations on the cells impacted subsequent depolarization in response to further stimulation. We identified tetraethylammonium (TEA)-sensitive native K+ channels as one of the mediators of this habituation phenotype. Finally, we used a theoretical model of habituation to elucidate some mechanistic aspects of the habituation response. In conclusion, we affirm that habituation is a time- and state-dependent biological strategy that can be adopted also by individual non-neuronal cells in response to repetitive stimuli.

The Computational Boundary of a "Self": Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition

All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, and molecular networks) that are themselves complex and competent in their own native contexts. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan creatures' bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger organism. In this article, I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: "Scale-Free Cognition." I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface - the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundary - a cognitive "light cone" which defines the scale and limits of its cognition. I hypothesize that higher level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress - the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity - the ability of all cells to form electrical networks that process information - suggest a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the basic drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology.

Cell learning

Although we often think of cells as small, simple building blocks of life, in fact they are highly complex and can perform a startling variety of functions. In our bodies, cells are programmed by complex differentiation pathways and are capable of responding to a bewildering range of chemical and physical signals. Free-living single-celled organisms, such as bacteria or protists, have to cope with varying environments, locate prey and potential mates, and escape from predators - all of the same tasks that a free-living animal is faced with. When animals face complex behavioral challenges, they rely on their cognitive abilities - the ability to learn from experience, to analyse a situation and choose an appropriate course of action. This ability is essential for survival and should, in principle, be a ubiquitous feature of all living things regardless of the complexity of the organism.

Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches

There is still a significant gap between our understanding of neural circuits and the behaviours they compute-i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci.15, 507-509. (doi:10.1038/nn.3043)). Cellular decision-making processes, learning, behaviour and memory formation-all that have been only associated with animals with neural systems-have also been observed in many unicellular aneural organisms, namely Physarum, Paramecium and Stentor (Tang & Marshall2018 Curr. Biol.28, R1180-R1184. (doi:10.1016/j.cub.2018.09.015)). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content8, 23-60. (doi:10.1152/ajplegacy.1902.8.1.23)) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al. 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli. Our results showed that an artificial neural network with multiple 'computational' neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

Substrate composition directs slime molds behavior

Cells, including unicellulars, are highly sensitive to external constraints from their environment. Amoeboid cells change their cell shape during locomotion and in response to external stimuli. Physarum polycephalum is a large multinucleated amoeboid cell that extends and develops pseudopods. In this paper, changes in cell behavior and shape were measured during the exploration of homogenous and non-homogenous environments that presented neutral, and nutritive and/or adverse substances. In the first place, we developed a fully automated image analysis method to measure quantitatively changes in both migration and shape. Then we measured various metrics that describe the area covered, the exploration dynamics, the migration rate and the slime mold shape. Our results show that: (1) Not only the nature, but also the spatial distribution of chemical substances affect the exploration behavior of slime molds; (2) Nutritive and adverse substances both slow down the exploration and prevent the formation of pseudopods; and (3) Slime mold placed in an adverse environment preferentially occupies previously explored areas rather than unexplored areas using mucus secretion as a buffer. Our results also show that slime molds migrate at a rate governed by the substrate up until they get within a critical distance to chemical substances.

Probing complexity: thermodynamics and computational mechanics approaches to origins studies

This paper proposes new avenues for origins research that apply modern concepts from stochastic thermodynamics, information thermodynamics and complexity science. Most approaches to the emergence of life prioritize certain compounds, reaction pathways, environments or phenomena. What they all have in common is the objective of reaching a state that is recognizably alive, usually positing the need for an evolutionary process. As with life itself, this correlates with a growth in the complexity of the system over time. Complexity often takes the form of an intuition or a proxy for a phenomenon that defies complete understanding. However, recent progress in several theoretical fields allows the rigorous computation of complexity. We thus propose that measurement and control of the complexity and information content of origins-relevant systems can provide novel insights that are absent in other approaches. Since we have no guarantee that the earliest forms of life (or alien life) used the same materials and processes as extant life, an appeal to complexity and information processing provides a more objective and agnostic approach to the search for life's beginnings. This paper gives an accessible overview of the three relevant branches of modern thermodynamics. These frameworks are not commonly applied in origins studies, but are ideally suited to the analysis of such non-equilibrium systems. We present proposals for the application of these concepts in both theoretical and experimental origins settings.