A Complex Hierarchy of Avoidance Behaviors in a Single-Cell Eukaryote

Plant intelligence dux: a comprehensive rebuttal of Kingsland and Taiz

Intelligence is a fundamental property for all life enabling an increased probability of survival and reproduction under wild circumstances. Kingsland and Taiz (2024) think that plants are not intelligent but seem unaware of the extensive literature about intelligence, memory, learning and chromatin topology in plants. Their views are consequently rejected. Their claim of fake quotations is shown to result from faulty reasoning and lack of understanding of practical biology. Their use of social media as scholarly evidence is unacceptable. Darwin's views on intelligence are described, and their pertinence to the adaptive responses of plants is discussed. Justifications for comments I have made concerning McClintock and her "thoughtful" cell, von Sachs writings as indicating purpose (teleonomy) to plant behaviour, Went and Thimann's allusions to plant intelligence and Bose legacy as the father of plant electrophysiology are described. These scientists were usually first in their field of knowledge, and their understanding was consequently deeper. The article finishes with a brief critical analysis of the 36 scientists who were used to condemn plant neurobiology as of no use. It is concluded that participants signed up to a false prospectus because contrary evidence was omitted.

Cellular cognition: How single cells learn using non-neural networks

Single cells can perform surprisingly complex behaviors and computations, including primitive forms of learning like habituation. New work highlighted here uses mathematical modeling to show that relatively simple biochemical networks can recapitulate many features of habituation in animals.

Biochemically plausible models of habituation for single-cell learning

The ability to learn is typically attributed to animals with brains. However, the apparently simplest form of learning, habituation, in which a steadily decreasing response is exhibited to a repeated stimulus, is found not only in animals but also in single-cell organisms and individual mammalian cells. Habituation has been codified from studies in both invertebrate and vertebrate animals as having ten characteristic hallmarks, seven of which involve a single stimulus. Here, we show by mathematical modeling that simple molecular networks, based on plausible biochemistry with common motifs of negative feedback and incoherent feedforward, can robustly exhibit all single-stimulus hallmarks. The models reveal how the hallmarks arise from underlying properties of timescale separation and reversal behavior of memory variables, and they reconcile opposing views of frequency and intensity sensitivity expressed within the neuroscience and cognitive science traditions. Our results suggest that individual cells may exhibit habituation behavior as rich as that which has been codified in multi-cellular animals with central nervous systems and that the relative simplicity of the biomolecular level may enhance our understanding of the mechanisms of learning.

A receptor-inactivation model for single-celled habituation in Stentor coeruleus

The single-celled ciliate Stentor coeruleus demonstrates habituation to mechanical stimuli, showing that even single cells can manifest a basic form of learning. Although the ability of Stentor to habituate has been extensively documented, the mechanism of learning is currently not known. Here we take a bottom-up approach and investigate a simple biochemistry-based model based on prior electrophysiological measurements in Stentor along with general properties of receptor molecules. In this model, a mechanoreceptor senses the stimulus and leads to channel opening to change membrane potential, with a sufficient change in polarization triggering an action potential that drives contraction. Receptors that are activated can become internalized, after which they can either be degraded or recycled back to the cell surface. This activity-dependent internalization provides a potential means for the cell to learn. Stochastic simulations of this model confirm that it is capable of showing habituation similar to what is seen in actual Stentor cells, including the lack of dishabituation by strong stimuli and the apparently step-like response of individual cells during habituation. The model also can account for several habituation hallmarks that a previous two-state Markov model could not, namely, the dependence of habituation rate on stimulus magnitude, which had to be added onto the two state model but arises naturally in the receptor inactivation model; the rate of response recovery after cessation of stimulation; the ability of high frequency stimulus sequences to drive faster habituation that results in a lower response probability, and the potentiation of habituation by repeated rounds of training and recovery. The model makes the prediction that application of high force stimuli that do not normally habituate should drive habituation to weaker stimuli due to decrease in the receptor number, which serves as an internal hidden variable. We confirmed this prediction using two new sets of experiments involving alternation of weak and strong stimuli. Furthermore, the model predicts that training with high force stimuli delays response recovery to low force stimuli, which aligns with our new experimental data. The model also predicts subliminal accumulation, wherein continuation of training even after habituation has reached asymptotic levels should lead to delayed response recovery, which was also confirmed by new experiments. The model is unable to account for the phenomenon of rate sensitivity, in which habituation caused by higher frequency stimuli is more easily reversed leading to a frequency dependence of response recovery. Such rate sensitivity has not been reported in Stentor . Here we carried out a new set of experiments which are consistent with the model's prediction of the lack of rate sensitivity. This work demonstrates how a simple model can suggest new ways to probe single-cell learning at an experimental level. Finally, we interpret the model in terms of a kernel estimator that the cell may use to guide its decisions about how to response to new stimuli as they arise based on information, or the lack thereof, from past stimuli.

Biological computations: Limitations of attractor-based formalisms and the need for transients

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, for e.g. via cell surface receptors or sensory organs. By integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.

Phylogeny, morphology, and behavior of the new ciliate species Stentor stipatus

The study of evolution at the cellular level traditionally has focused on the evolution of metabolic pathways, endomembrane systems, and genomes, but there has been increasing interest in evolution of more complex cellular structures and behaviors, particularly in the eukaryotes. Ciliates have major advantages for such studies due to their easily visible surface patterning and their dramatic and complex behaviors that can be easily analyzed. Among the ciliates, the genus Stentor epitomizes the features that are useful for studying evolution: they are widespread in freshwater environments, easy to visualize because of their large size, and capable of complex behaviors such as learning, decision-making, and phototaxis. Here, we introduce the discovery of a new species within this genus: Stentor stipatus, so named for their distinctive dark brown aggregates. We present morphological, phylogenetic, ecological, and behavioral characterizations of these cells. The S. stipatus clade has a bootstrap value of 93 and is phylogenetically distinct from S. amethystinus, the closest related species which shares a sequence similarity of 98.9%. S. stipatus is capable of phototaxis and can also habituate more quickly than S. coeruleus, the Stentor species in which most habituation studies have previously been conducted. These findings expand our understanding of Stentor species diversity, natural history, and demonstrate common principles of complex behavior that are present in single-celled organisms.

The sensual cell: Feeling and affect in unicellular species

Our previous efforts to probe the complex, rich experiential lives of unicellular species have focused on the origins of consciousness (Reber, 2019) and the biomolecular processes that underlie sentience (Reber et al., 2023). Implied, but unexplored, was the assumption that these cognitive functions and associated unicellular organismal behaviors were linked with and often driven by affect, feelings, sensual experiences. In this essay we dig more deeply into these valenced (We're using the term valence here to cover the aspects of sensory experiences that have evaluative elements, are experienced as positive or negative ─ those where this affective, internal representation is an essential element in how the input is interpreted and responded to.) self-referencing features. In the first part, we examine the empirical evidence for various sensual experiences that have been identified. In the second part, we look at other features of prokaryote life that appear to also have affective, valenced elements but where the data to support the proposition aren't as strong. We engage in some informed speculation about these phenomena.

Life Is Uncertain: Inherent Variability Exhibited by Organisms, and at Higher Levels of Biological Organization

Organisms act stochastically. A not uncommon view in the ecological literature is that this is mainly due to the observer having insufficient information or a stochastic environment-and not partly because organisms themselves respond with inherent unpredictability. In this study, I compile the evidence that contradicts that view. Organisms generate uncertainty internally, which results in irreducible stochastic responses. I consider why: for instance, stochastic responses are associated with greater adaptability to changing environments and resource availability. Over longer timescales, biologically generated uncertainty influences behavior, evolution, and macroecological processes. Indeed, it could be stated that organisms are systems defined by the internal generation, magnification, and record-keeping of uncertainty as inputs to responses. Important practical implications arise if organisms can indeed be defined by an association with specific classes of inherent uncertainty: not least that isolating those signatures then provides a potential means for detecting life, for considering the forms that life could theoretically take, and for exploring the wider limits to how life might become distributed. These are all fundamental goals in astrobiology.

Bioelectric control of locomotor gaits in the walking ciliate Euplotes

Diverse animal species exhibit highly stereotyped behavioral actions and locomotor sequences as they explore their natural environments. In many such cases, the neural basis of behavior is well established, where dedicated neural circuitry contributes to the initiation and regulation of certain response sequences. At the microscopic scale, single-celled eukaryotes (protists) also exhibit remarkably complex behaviors and yet are completely devoid of nervous systems. Here, to address the question of how single cells control behavior, we study locomotor patterning in the exemplary hypotrich ciliate Euplotes, a highly polarized cell, which actuates a large number of leg-like appendages called cirri (each a bundle of ∼25-50 cilia) to swim in fluids or walk on surfaces. As it navigates its surroundings, a walking Euplotes cell is routinely observed to perform side-stepping reactions, one of the most sophisticated maneuvers ever observed in a single-celled organism. These are spontaneous and stereotyped reorientation events involving a transient and fast backward motion followed by a turn. Combining high-speed imaging with simultaneous time-resolved electrophysiological recordings, we show that this complex coordinated motion sequence is tightly regulated by rapid membrane depolarization events, which orchestrate the activity of different cirri on the cell. Using machine learning and computer vision methods, we map detailed measurements of cirri dynamics to the cell's membrane bioelectrical activity, revealing a differential response in the front and back cirri. We integrate these measurements with a minimal model to understand how Euplotes-a unicellular organism-manipulates its membrane potential to achieve real-time control over its motor apparatus.

From reinforcement learning to agency: Frameworks for understanding basal cognition

Organisms play, explore, and mimic those around them. Is there a purpose to this behavior? Are organisms just behaving, or are they trying to achieve goals? We believe this is a false dichotomy. To that end, to understand organisms, we attempt to unify two approaches for understanding complex agents, whether evolved or engineered. We argue that formalisms describing multiscale competencies and goal-directedness in biology (e.g., TAME), and reinforcement learning (RL), can be combined in a symbiotic framework. While RL has been largely focused on higher-level organisms and robots of high complexity, TAME is naturally capable of describing lower-level organisms and minimal agents as well. We propose several novel questions that come from using RL/TAME to understand biology as well as ones that come from using biology to formulate new theory in AI. We hope that the research programs proposed in this piece shape future efforts to understand biological organisms and also future efforts to build artificial agents.

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.

Cellular computation and cognition

Contemporary neural network models often overlook a central biological fact about neural processing: that single neurons are themselves complex, semi-autonomous computing systems. Both the information processing and information storage abilities of actual biological neurons vastly exceed the simple weighted sum of synaptic inputs computed by the "units" in standard neural network models. Neurons are eukaryotic cells that store information not only in synapses, but also in their dendritic structure and connectivity, as well as genetic "marking" in the epigenome of each individual cell. Each neuron computes a complex nonlinear function of its inputs, roughly equivalent in processing capacity to an entire 1990s-era neural network model. Furthermore, individual cells provide the biological interface between gene expression, ongoing neural processing, and stored long-term memory traces. Neurons in all organisms have these properties, which are thus relevant to all of neuroscience and cognitive biology. Single-cell computation may also play a particular role in explaining some unusual features of human cognition. The recognition of the centrality of cellular computation to "natural computation" in brains, and of the constraints it imposes upon brain evolution, thus has important implications for the evolution of cognition, and how we study it.

Design principles of 3D epigenetic memory systems

Cells remember their identities, in part, by using epigenetic marks-chemical modifications placed along the genome. How can mark patterns remain stable over cell generations despite their constant erosion by replication and other processes? We developed a theoretical model that reveals that three-dimensional (3D) genome organization can stabilize epigenetic memory as long as (i) there is a large density difference between chromatin compartments, (ii) modifying "reader-writer" enzymes spread marks in three dimensions, and (iii) the enzymes are limited in abundance relative to their histone substrates. Analogous to an associative memory that encodes memory in neuronal connectivity, mark patterns are encoded in a 3D network of chromosomal contacts. Our model provides a unified account of diverse observations and reveals a key role of 3D genome organization in epigenetic memory.

An evidence-based critical review of the mind-brain identity theory

In the philosophy of mind, neuroscience, and psychology, the causal relationship between phenomenal consciousness, mentation, and brain states has always been a matter of debate. On the one hand, material monism posits consciousness and mind as pure brain epiphenomena. One of its most stringent lines of reasoning relies on a 'loss-of-function lesion premise,' according to which, since brain lesions and neurochemical modifications lead to cognitive impairment and/or altered states of consciousness, there is no reason to doubt the mind-brain identity. On the other hand, dualism or idealism (in one form or another) regard consciousness and mind as something other than the sole product of cerebral activity pointing at the ineffable, undefinable, and seemingly unphysical nature of our subjective qualitative experiences and its related mental dimension. Here, several neuroscientific findings are reviewed that question the idea that posits phenomenal experience as an emergent property of brain activity, and argue that the premise of material monism is based on a logical correlation-causation fallacy. While these (mostly ignored) findings, if considered separately from each other, could, in principle, be recast into a physicalist paradigm, once viewed from an integral perspective, they substantiate equally well an ontology that posits mind and consciousness as a primal phenomenon.

Design and Simulation of a Multilayer Chemical Neural Network That Learns via Backpropagation

The design and implementation of adaptive chemical reaction networks, capable of adjusting their behavior over time in response to experience, is a key goal for the fields of molecular computing and DNA nanotechnology. Mainstream machine learning research offers powerful tools for implementing learning behavior that could one day be realized in a wet chemistry system. Here we develop an abstract chemical reaction network model that implements the backpropagation learning algorithm for a feedforward neural network whose nodes employ the nonlinear "leaky rectified linear unit" transfer function. Our network directly implements the mathematics behind this well-studied learning algorithm, and we demonstrate its capabilities by training the system to learn a linearly inseparable decision surface, specifically, the XOR logic function. We show that this simulation quantitatively follows the definition of the underlying algorithm. To implement this system, we also report ProBioSim, a simulator that enables arbitrary training protocols for simulated chemical reaction networks to be straightforwardly defined using constructs from the host programming language. This work thus provides new insight into the capabilities of learning chemical reaction networks and also develops new computational tools to simulate their behavior, which could be applied in the design and implementations of adaptive artificial life.

Conditioned Place Avoidance in the Planaria Schmidtea mediterranea: A Pre-clinical Invertebrate Model of Anxiety-Related Disorders

The objective of the present study was to develop a model of avoidance learning and its extinction in planarians (Schmidtea mediterranea). Based on previous experiments showing conditioned place preference, we developed a procedure to investigate conditioned place avoidance (CPA) using shock as an unconditioned stimulus (US) and an automated tracking system to record the animals' behaviour. In Experiment 1, we assessed the unconditioned properties of different shock intensities by measuring post shock activity. In two subsequent experiments we investigated CPA using different designs, surfaces as conditioned stimuli (CSs; rough and smooth), and different US intensities (5V and 10V). In general, we observed the successful development of CPA. However, CPA was stronger with higher shock intensities, and we found that, in our preparation, a rough surface is best at entering into an association with the shock than a smooth surface. Finally, we also observed extinction of CPA. The evidence of CPA and its extinction in flatworms validates the planaria as a pre-clinical model for the study of avoidance learning, a hallmark of anxiety disorders.

The molecular memory code and synaptic plasticity: A synthesis

The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.

Cellular learning: Habituation sans neurons in a unicellular organism

Stentor coeruleus cells stochastically switch between non-responsive (contracted) and responsive (extended) states. Learning is accomplished via habituation, in which the internal model is updated to reflect the current environment by tuning the transition rates according to the time series properties of mechanical stimuli.

Single-cell analysis of habituation in Stentor coeruleus

Although learning is often viewed as a unique feature of organisms with complex nervous systems, single-celled organisms also demonstrate basic forms of learning. The giant ciliate Stentor coeruleus responds to mechanical stimuli by contracting into a compact shape, presumably as a defense mechanism. When a Stentor cell is repeatedly stimulated at a constant level of force, it will learn to ignore that stimulus but will still respond to stronger stimuli. Prior studies of habituation in Stentor reported a graded response, suggesting that cells transition through a continuous range of response probabilities. By analyzing single cells using an automated apparatus to deliver calibrated stimuli, we find that habituation occurs via a single step-like switch in contraction probability within each cell, with the graded response in a population arising from the random distribution of switching times in individual cells. This step-like response allows Stentor behavior to be represented by a simple two-state model whose parameters can be estimated from experimental measurements. We find that transition rates depend on stimulus force and also on the time between stimuli. The ability to measure the behavior of the same cell to the same stimulus allowed us to quantify the functional heterogeneity among single cells. Together, our results suggest that the behavior of Stentor is governed by a two-state stochastic machine whose transition rates are sensitive to the time series properties of the input stimuli.

Cooperative hydrodynamics accompany multicellular-like colonial organization in the unicellular ciliate Stentor

Evolution of multicellularity from early unicellular ancestors is arguably one of the most important transitions since the origin of life1,2. Multicellularity is often associated with higher nutrient uptake3, better defense against predation, cell specialization and better division of labor4. While many single-celled organisms exhibit both solitary and colonial existence3,5,6, the organizing principles governing the transition and the benefits endowed are less clear. Using the suspension-feeding unicellular protist Stentor coeruleus, we show that hydrodynamic coupling between proximal neighbors results in faster feeding flows that depend on the separation between individuals. Moreover, we find that the accrued benefits in feeding current enhancement are typically asymmetric- individuals with slower solitary currents gain more from partnering than those with faster currents. We find that colony-formation is ephemeral in Stentor and individuals in colonies are highly dynamic unlike other colony-forming organisms like Volvox carteri 3. Our results demonstrate benefits endowed by the colonial organization in a simple unicellular organism and can potentially provide fundamental insights into the selective forces favoring early evolution of multicellular organization.

Switching of behavioral modes and their modulation by a geometrical cue in the ciliate Stentor coeruleus

Protists ubiquitously live in nature and play key roles in the food web chain. Their habitats consist of various geometrical structures, such as porous media and rigid surfaces, affecting their motilities. A kind of protist, Stentor coeruleus, exhibits free swimming and adhering for feeding. Under environmental and culture conditions, these organisms are often found in sediments with complex geometries. The determination of anchoring location is essential for their lives. However, the factors that induce the behavioral transition from swimming to adhering are still unknown. In this study, we quantitatively characterized the behavioral transitions in S. coeruleus and observed the behavior in a chamber with dead ends made by a simple structure mimicking the environmental structures. As a result, the cell adheres and feeds in narrow spaces between the structure and the chamber wall. It may be reasonable for the organism to hide itself from predators and capture prey in these spaces. The behavioral strategy for the exploration and exploitation of spaces with a wide variety of geometries in their habitats is discussed.

Operant conditioning of stochastic chemical reaction networks

Adapting one's behavior to environmental conditions and past experience is a key trait of living systems. In the biological world, there is evidence for adaptive behaviors such as learning even in naturally occurring, non-neural, single-celled organisms. In the bioengineered world, advances in synthetic cell engineering and biorobotics have created the possibility of implementing lifelike systems engineered from the bottom up. This will require the development of programmable control circuitry for such biomimetic systems that is capable of realizing such non-trivial and adaptive behavior, including modification of subsequent behavior in response to environmental feedback. To this end, we report the design of novel stochastic chemical reaction networks capable of probabilistic decision-making in response to stimuli. We show that a simple chemical reaction network motif can be tuned to produce arbitrary decision probabilities when choosing between two or more responses to a stimulus signal. We further show that simple feedback mechanisms from the environment can modify these probabilities over time, enabling the system to adapt its behavior dynamically in response to positive or negative reinforcement based on its decisions. This system thus acts as a form of operant conditioning of the chemical circuit, in the sense that feedback provided based on decisions taken by the circuit form the basis of the learning process. Our work thus demonstrates that simple chemical systems can be used to implement lifelike behavior in engineered biomimetic systems.

A unicellular walker controlled by a microtubule-based finite-state machine

Cells are complex biochemical systems whose behaviors emerge from interactions among myriad molecular components. Computation is often invoked as a general framework for navigating this cellular complexity. However, it is unclear how cells might embody computational processes such that the theories of computation, including finite-state machine models, could be productively applied. Here, we demonstrate finite-state-machine-like processing embodied in cells using the walking behavior of Euplotes eurystomus, a ciliate that walks across surfaces using fourteen motile appendages (cirri). We found that cellular walking entails regulated transitions among a discrete set of gait states. The set of observed transitions decomposes into a small group of high-probability, temporally irreversible transitions and a large group of low-probability, time-symmetric transitions, thus revealing stereotypy in the sequential patterns of state transitions. Simulations and experiments suggest that the sequential logic of the gait is functionally important. Taken together, these findings implicate a finite-state-machine-like process. Cirri are connected by microtubule bundles (fibers), and we found that the dynamics of cirri involved in different state transitions are associated with the structure of the fiber system. Perturbative experiments revealed that the fibers mediate gait coordination, suggesting a mechanical basis of gait control.

Vitalism and cognition in a conscious universe

According to the current scientific paradigm, what we call ‘life’, ‘mind’, and ‘consciousness’ are considered epiphenomenal occurrences, or emergent properties or functions of matter and energy. Science does not associate these with an inherent and distinct existence beyond a materialistic/energetic conception. ‘Life’ is a word pointing at cellular and multicellular processes forming organisms capable of specific functions and skills. ‘Mind’ is a cognitive ability emerging from a matrix of complex interactions of neuronal processes, while ‘consciousness’ is an even more elusive concept, deemed a subjective epiphenomenon of brain activity. Historically, however, this has not always been the case, even in the scientific and academic context. Several prominent figures took vitalism seriously, while some schools of Western philosophical idealism and Eastern traditions promoted conceptions in which reality is reducible to mind or consciousness rather than matter. We will argue that current biological sciences did not falsify these alternative paradigms and that some forms of vitalism could be linked to some forms of idealism if we posit life and cognition as two distinct aspects of consciousness preeminent over matter. However, we will not argue in favor of vitalistic and idealistic conceptions. Rather, contrary to a physicalist doctrine, these were and remain coherent worldviews and cannot be ruled out by modern science.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

An RNA-Based Theory of Natural Universal Computation

Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal computation, i.e., Turing-equivalent in scope. Generic finite-dimensional dynamical systems (which encompass most models of neural networks, intracellular signaling cascades, and gene regulatory networks) fall short of universal computation, but are assumed to be capable of explaining cognition and development. I present a class of models that bridge two concepts from distant fields: combinatory logic (or, equivalently, lambda calculus) and RNA molecular biology. A set of basic RNA editing rules can make it possible to compute any computable function with identical algorithmic complexity to that of Turing machines. The models do not assume extraordinarily complex molecular machinery or any processes that radically differ from what we already know to occur in cells. Distinct independent enzymes can mediate each of the rules and RNA molecules solve the problem of parenthesis matching through their secondary structure. In the most plausible of these models all of the editing rules can be implemented with merely cleavage and ligation operations at fixed positions relative to predefined motifs. This demonstrates that universal computation is well within the reach of molecular biology. It is therefore reasonable to assume that life has evolved - or possibly began with - a universal computer that yet remains to be discovered. The variety of seemingly unrelated computational problems across many scales can potentially be solved using the same RNA-based computation system. Experimental validation of this theory may immensely impact our understanding of memory, cognition, development, disease, evolution, and the early stages of life.

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.

Robust finite automata in stochastic chemical reaction networks

Finite-state automata (FSA) are simple computational devices that can nevertheless illustrate interesting behaviours. We propose that FSA can be employed as control circuits for engineered stochastic biological and biomolecular systems. We present an implementation of FSA using counts of chemical species in the range of hundreds to thousands, which is relevant for the counts of many key molecules such as mRNAs in prokaryotic cells. The challenge here is to ensure a robust representation of the current state in the face of stochastic noise. We achieve this by using a multistable approximate majority algorithm to stabilize and store the current state of the system. Arbitrary finite state machines can thus be compiled into robust stochastic chemical automata. We present two variants: one that consumes its input signals to initiate state transitions and one that does not. We characterize the state change dynamics of these systems and demonstrate their application to solve the four-bit binary square root problem. Our work lays the foundation for the use of chemical automata as control circuits in bioengineered systems and biorobotics.

Information and the single cell

Understanding the evolution of cognition requires an understanding of the costs and benefits of neural computation. This requires analysis of neuronal circuitry in terms of information-processing efficiency, ultimately cashed out in terms of ATP expenditures relative to adaptive problem-solving abilities. Despite a preoccupation in neuroscience with the synapse as the source of stored neural information, it is clear that, along with synaptic weights and electrochemical dynamics, neurons have multiple mechanisms which store and process information, including 'wetware' (protein phosphorylation, gene transcription, and so on) and cell morphology (dendritic form). Insights into non-synaptic information-processing can be gained by examining the surprisingly complex abilities of single-celled organisms ('cellular cognition') because neurons share many of the same abilities. Cells provide the fundamental level at which information processing interfaces with gene expression, and cell-internal information-processing mechanisms are both powerful and energetically efficient. Understanding cellular computation should be a central goal of research on cognitive evolution.

Towards the Idea of Molecular Brains

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.

Bioluminescence and Photoreception in Unicellular Organisms: Light-Signalling in a Bio-Communication Perspective

Bioluminescence, the emission of light catalysed by luciferases, has evolved in many taxa from bacteria to vertebrates and is predominant in the marine environment. It is now well established that in animals possessing a nervous system capable of integrating light stimuli, bioluminescence triggers various behavioural responses and plays a role in intra- or interspecific visual communication. The function of light emission in unicellular organisms is less clear and it is currently thought that it has evolved in an ecological framework, to be perceived by visual animals. For example, while it is thought that bioluminescence allows bacteria to be ingested by zooplankton or fish, providing them with favourable conditions for growth and dispersal, the luminous flashes emitted by dinoflagellates may have evolved as an anti-predation system against copepods. In this short review, we re-examine this paradigm in light of recent findings in microorganism photoreception, signal integration and complex behaviours. Numerous studies show that on the one hand, bacteria and protists, whether autotrophs or heterotrophs, possess a variety of photoreceptors capable of perceiving and integrating light stimuli of different wavelengths. Single-cell light-perception produces responses ranging from phototaxis to more complex behaviours. On the other hand, there is growing evidence that unicellular prokaryotes and eukaryotes can perform complex tasks ranging from habituation and decision-making to associative learning, despite lacking a nervous system. Here, we focus our analysis on two taxa, bacteria and dinoflagellates, whose bioluminescence is well studied. We propose the hypothesis that similar to visual animals, the interplay between light-emission and reception could play multiple roles in intra- and interspecific communication and participate in complex behaviour in the unicellular world.

Physical intelligence as a new paradigm

Intelligence of physical agents, such as human-made (e.g., robots, autonomous cars) and biological (e.g., animals, plants) ones, is not only enabled by their computational intelligence (CI) in their brain, but also by their physical intelligence (PI) encoded in their body. Therefore, it is essential to advance the PI of human-made agents as much as possible, in addition to their CI, to operate them in unstructured and complex real-world environments like the biological agents. This article gives a perspective on what PI paradigm is, when PI can be more significant and dominant in physical and biological agents at different length scales and how bioinspired and abstract PI methods can be created in agent bodies. PI paradigm aims to synergize and merge many research fields, such as mechanics, materials science, robotics, mechanical design, fluidics, active matter, biology, self-assembly and collective systems, to enable advanced PI capabilities in human-made agent bodies, comparable to the ones observed in biological organisms. Such capabilities would progress the future robots and other machines beyond what can be realized using the current frameworks.

Ciliate behavior: blueprints for dynamic cell biology and microscale robotics

Place a drop of pond water under the microscope, and you will likely find an ocean of extraordinary and diverse single-celled organisms called ciliates. This remarkable group of single-celled organisms wield microtubules, active systems, electrical signaling, and chemical sensors to build intricate geometrical structures and perform complex behaviors that can appear indistinguishable from those of macroscopic animals. Advances in computer vision and machine learning are making it possible to completely digitize and track the dynamics of complex ciliates and mine these data for the hidden structure, patterns, and motifs that are responsible for their behaviors. By deconstructing the diversity of ciliate behaviors in the natural world, themes for organizing and controlling matter at the microscale are beginning to take hold, suggesting new modular approaches for the design of autonomous molecular machines that emulate nature’s finest examples.

Cytoskeletal prestress: The cellular hallmark in mechanobiology and mechanomedicine

Increasing evidence demonstrates that mechanical forces, in addition to soluble molecules, impact cell and tissue functions in physiology and diseases. How living cells integrate mechanical signals to perform appropriate biological functions is an area of intense investigation. Here, we review the evidence of the central role of cytoskeletal prestress in mechanotransduction and mechanobiology. Elevating cytoskeletal prestress increases cell stiffness and reinforces cell stiffening, facilitates long-range cytoplasmic mechanotransduction via integrins, enables direct chromatin stretching and rapid gene expression, spurs embryonic development and stem cell differentiation, and boosts immune cell activation and killing of tumor cells whereas lowering cytoskeletal prestress maintains embryonic stem cell pluripotency, promotes tumorigenesis and metastasis of stem cell-like malignant tumor-repopulating cells, and elevates drug delivery efficiency of soft-tumor-cell-derived microparticles. The overwhelming evidence suggests that the cytoskeletal prestress is the governing principle and the cellular hallmark in mechanobiology. The application of mechanobiology to medicine (mechanomedicine) is rapidly emerging and may help advance human health and improve diagnostics, treatment, and therapeutics of diseases.

Sensing the world and its dangers: An evolutionary perspective in neuroimmunology

Detecting danger is key to the survival and success of all species. Animal nervous and immune systems cooperate to optimize danger detection. Preceding studies have highlighted the benefits of bringing neurons into the defense game, including regulation of immune responses, wound healing, pathogen control, and survival. Here, we summarize the body of knowledge in neuroimmune communication and assert that neuronal participation in the immune response is deeply beneficial in each step of combating infection, from inception to resolution. Despite the documented tight association between the immune and nervous systems in mammals or invertebrate model organisms, interdependence of these two systems is largely unexplored across metazoans. This review brings a phylogenetic perspective of the nervous and immune systems in the context of danger detection and advocates for the use of non-model organisms to diversify the field of neuroimmunology. We identify key taxa that are ripe for investigation due to the emergence of key evolutionary innovations in their immune and nervous systems. This novel perspective will help define the primordial principles that govern neuroimmune communication across taxa.

Origins of eukaryotic excitability

All living cells interact dynamically with a constantly changing world. Eukaryotes, in particular, evolved radically new ways to sense and react to their environment. These advances enabled new and more complex forms of cellular behaviour in eukaryotes, including directional movement, active feeding, mating, and responses to predation. But what are the key events and innovations during eukaryogenesis that made all of this possible? Here we describe the ancestral repertoire of eukaryotic excitability and discuss five major cellular innovations that enabled its evolutionary origin. The innovations include a vastly expanded repertoire of ion channels, the emergence of cilia and pseudopodia, endomembranes as intracellular capacitors, a flexible plasma membrane and the relocation of chemiosmotic ATP synthesis to mitochondria, which liberated the plasma membrane for more complex electrical signalling involved in sensing and reacting. We conjecture that together with an increase in cell size, these new forms of excitability greatly amplified the degrees of freedom associated with cellular responses, allowing eukaryotes to vastly outperform prokaryotes in terms of both speed and accuracy. This comprehensive new perspective on the evolution of excitability enriches our view of eukaryogenesis and emphasizes behaviour and sensing as major contributors to the success of eukaryotes. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.

Biomolecular Basis of Cellular Consciousness via Subcellular Nanobrains

Cells emerged at the very beginning of life on Earth and, in fact, are coterminous with life. They are enclosed within an excitable plasma membrane, which defines the outside and inside domains via their specific biophysical properties. Unicellular organisms, such as diverse protists and algae, still live a cellular life. However, fungi, plants, and animals evolved a multicellular existence. Recently, we have developed the cellular basis of consciousness (CBC) model, which proposes that all biological awareness, sentience and consciousness are grounded in general cell biology. Here we discuss the biomolecular structures and processes that allow for and maintain this cellular consciousness from an evolutionary perspective.

Emergent Robotic Personality Traits via Agent-Based Simulation of Abstract Social Environments

This paper discusses the creation of an agent-based simulation model for interactive robotic faces, built based on data from physical human–robot interaction experiments, to explore hypotheses around how we might create emergent robotic personality traits, rather than pre-scripted ones based on programmatic rules. If an agent/robot can visually attend and behaviorally respond to social cues in its environment, and that environment varies, then idiosyncratic behavior that forms the basis of what we call a “personality” should theoretically be emergent. Here, we evaluate the stability of behavioral learning convergence in such social environments to test this idea. We conduct over 2000 separate simulations of an agent-based model in scaled-down, abstracted forms of the environment, each one representing an “experiment”, to see how different parameters interact to affect this process. Our findings suggest that there may be systematic dynamics in the learning patterns of an agent/robot in social environments, as well as significant interaction effects between the environmental setup and agent perceptual model. Furthermore, learning from deltas (Markovian approach) was more effective than only considering the current state space. We discuss the implications for HRI research, the design of interactive robotic faces, and the development of more robust theoretical frameworks of social interaction.

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.

Reconsidering the evidence for learning in single cells

The question of whether single cells can learn led to much debate in the early 20th century. The view prevailed that they were capable of non-associative learning but not of associative learning, such as Pavlovian conditioning. Experiments indicating the contrary were considered either non-reproducible or subject to more acceptable interpretations. Recent developments suggest that the time is right to reconsider this consensus. We exhume the experiments of Beatrice Gelber on Pavlovian conditioning in the ciliate Paramecium aurelia, and suggest that criticisms of her findings can now be reinterpreted. Gelber was a remarkable scientist whose absence from the historical record testifies to the prevailing orthodoxy that single cells cannot learn. Her work, and more recent studies, suggest that such learning may be evolutionarily more widespread and fundamental to life than previously thought and we discuss the implications for different aspects of biology.

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.

Integrated information as a possible basis for plant consciousness

It is commonly assumed that plants do not possess consciousness. Since the criterion for this assumption is usually human consciousness this assumption represents a top down attitude. It is obvious that plants are not animals and using animal criteria of consciousness will lead to its rejection in plants. However using a bottom up evolutionary approach and a leading theory of consciousness, Integrated Information Theory, we report that we find evidence that indicates that plant meristems act in a conscious fashion although probably at the level of minimal consciousness. Since many plants contain multiple meristems these observations highlight a very different evolutionary approach to consciousness in biological organisms.

New contributions to the phylogeny of the ciliate class Heterotrichea (Protista, Ciliophora): analyses at family-genus level and new evolutionary hypotheses

Heterotrichous ciliates play an important role in aquatic ecosystem energy flow processes and many are model organisms for research in cytology, regenerative biology, and toxicology. In the present study, we combine both morphological and molecular data to infer phylogenetic relationships at family-genus level and propose new evolutionary hypotheses for the class Heterotrichea. The main results include: (1) 96 new ribosomal DNA sequences from 36 populations, representing eight families and 13 genera, including three poorly annotated genera, Folliculinopsis, Ampullofolliculina and Linostomella; (2) the earliest-branching families are Spirostomidae in single-gene trees and Peritromidae in the concatenated tree, but the family Peritromidae probably represents the basal lineage based on its possession of many "primitive" morphological characters; (3) some findings in molecular trees are not supported by morphological evidence, such as the family Blepharismidae is one of the most recent branches and the relationship between Fabreidae and Folliculinidae is very close; (4) the systematic positions of Condylostomatidae, Climacostomidae, and Gruberiidae remain uncertain based either on morphological or molecular data; and (5) the monophyly of each genus included in the present study is supported by the molecular phylogenetic trees, except for Blepharisma in the SSU rDNA tree and Folliculina in the ITS1-5.8S-ITS2 tree.

Cellular Cognition: Sequential Logic in a Giant Protist

Quantitative analysis of the giant ciliate Stentor roeselii shows that a single cell can make decisions, based on the ability to switch between several different behaviors in a non-random order.

Extended cognition in plants: is it possible?

Plants do not possess brains or neurons. However, they present astonishingly complex behaviors such as information acquisition, memory, learning, decision making, etc., which helps these sessile organisms deal with their ever-changing environments. As a consequence, they have been proposed to be cognitive and intelligent, an idea which is becoming increasingly accepted. However, how plant cognition could operate without a nervous central system remains poorly understood and new insights on this topic are urgently needed. According to the Extended Cognition hypothesis, cognition may also occur beyond the limits of the body, encompassing objects from the environment. This was shown possible in humans and spiders, who actively manipulate their external environment to extend their cognitive capacity. Here, we propose that extended cognition may also be found in plants and could partly explain the complexity of plant behavior. We suggest that plants can extend their cognitive abilities to the environment they manipulate through the root influence zone and the mycorrhizal fungi that associate with them. The possibility of a cognitive process involving organisms from different kingdoms is exciting and worthwhile exploring as it may provide key insights into the origin and evolution of cognition.

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.