It has been experimentally demonstrated that, under alkaline conditions, silica is able to induce the formation of mineral self-assembled inorganic-inorganic composite materials similar in morphology, texture and nanostructure to the hybrid biomineral structures that, millions of years later, life was able to self-organize. These mineral self-organized structures (MISOS) have been also shown to work as effective catalyzers for prebiotic chemical reactions and to easily create compartmentalization within the solutions where they form. We reason that, during the very earliest history of this planet, there was a geochemical scenario that inevitably led to the existence of a large-scale factory of simple and complex organic compounds, many of which were relevant to prebiotic chemistry. The factory is built on a silica-rich high-pH ocean and is powered by two main factors: a) a quasi-infinite source of simple carbon molecules synthesized abiotically from reactions associated with serpentinization, or transported from meteorites and produced from their impact on that alkaline ocean, and b) the formation of self-organized silica-metal mineral composites that catalyze the condensation of simple molecules in a methane-rich reduced atmosphere. We discuss the plausibility of this geochemical scenario, review the details of the formation of MISOS and its catalytic properties and the transition towards a slightly alkaline to neutral ocean.

The purpose of this investigation is to demonstrate that one is unable to understand the operation of the brain without taking top-down effects into account. This is demonstrated by looking in turn at evolutionary and developmental aspects, then at functional aspects related to sensory systems, learning processes, and motor processes that lead to action on the world. It is also clear in terms of the effects of a society on brains located in that society. The possibility of top down affects exists both because of multiple realisability of higher level processes at lower levels, and because lower level elements are adapted to perform their higher level functions. These top-down processes validate a non-reductionist approach to how the brain works.

This paper addresses the challenges faced by multiscale formulations of the variational (free energy) approach to dynamics that obtain for large-scale ensembles. We review a framework for modelling complex adaptive control systems for multiscale free energy bounding organism–niche dynamics, thereby integrating the modelling strategies and heuristics of variational neuroethology with a broader perspective on the ecological nestedness of biotic systems. We extend the multiscale variational formulation beyond the action–perception loops of individual organisms by appealing to the variational approach to niche construction to explain the dynamics of coupled systems constituted by organisms and their ecological niche. We suggest that the statistical robustness of living systems is inherited, in part, from their eco-niches, as niches help coordinate dynamical patterns across larger spatiotemporal scales. We call this approach variational ecology. We argue that, when applied to cultural animals such as humans, variational ecology enables us to formulate not just a physics of individual minds, but also a physics of interacting minds across spatial and temporal scales – a physics of sentient systems that range from cells to societies.

The paper is devoted to the relationship between psychophysics and physics of mind. The basic trends in psychophysics development are briefly discussed with special attention focused on Teghtsoonian's hypotheses. These hypotheses pose the concept of the universality of inner psychophysics and enable us to speak about psychological space as an individual object with its own properties. Turning to the two-component description of human behavior I. Lubashevsky (2017) [9] the notion of mental space is formulated and human perception of external stimuli is treated as the emergence of the corresponding images in the mental space. On one hand, these images are caused by external stimuli and their magnitude bears the information about the intensity of the corresponding stimuli. On the other hand, the individual structure of such images as well as their persistence after emergence is determined only by the properties of mental space on its own. Finally, the mental operations of image comparison and their scaling are defined in a way allowing for the bounded capacity of human cognition. As demonstrated, the developed theory of stimulus perception is able to explain the basic regularities of psychophysics, e.g., (i) the regression and range effects leading to the overestimation of weak stimuli and the underestimation of strong stimuli, (ii) scalar variability (Weber's and Ekman' laws), and (iii) the sequential (memory) effects. As the final result, a solution to the Fechner–Stevens dilemma is proposed. This solution posits that Fechner's logarithmic law is not a consequences of Weber's law but stems from the interplay of uncertainty in evaluating stimulus intensities and the multi-step scaling required to overcome the stimulus incommensurability.

Collective or group intelligence is manifested in the fact that a team of cooperating agents can solve problems more efficiently than when those agents work in isolation. Although cooperation is, in general, a successful problem solving strategy, it is not clear whether it merely speeds up the time to find the solution, or whether it alters qualitatively the statistical signature of the search for the solution. Here we review and offer insights on two agent-based models of distributed cooperative problem-solving systems, whose task is to solve a cryptarithmetic puzzle. The first model is the imitative learning search in which the agents exchange information on the quality of their partial solutions to the puzzle and imitate the most successful agent in the group. This scenario predicts a very poor performance in the case imitation is too frequent or the group is too large, a phenomenon akin to Groupthink of social psychology. The second model is the blackboard organization in which agents read and post hints on a public blackboard. This brainstorming scenario performs the best when there is a stringent limit to the amount of information that is exhibited on the board. Both cooperative scenarios produce a substantial speed up of the time to solve the puzzle as compared with the situation where the agents work in isolation. The statistical signature of the search, however, is the same as that of the independent search.

Research in origins of life is an intrinsically multi-disciplinary field, aimed at finding answers to the formidably complex problem of understanding the emergence of life from the modern versions of Charles Darwin's celebrated “primordial soup”. In the last few years, thanks to the increasing computational power and the development of sophisticated theoretical and numerical methods, several computational chemistry and physics groups have invested this field, providing new microscopic insights on fundamental prebiotic chemistry phenomena possibly occurring in the early Earth and outer space. This review presents the most successful and powerful approaches in computational chemistry, and the main results thus obtained in prebiotic chemistry and origins of life. The aim of this work is both to describe the state-of-the-art in computational prebiotic chemistry, possibly useful both to theorists and experimentalists in origins of life research, and to suggest future directions and new perspectives offered by modern simulation tools.

This paper presents a theory of the basic operation of mind, Information Dynamics of Thinking, which is intended for computational implementation and thence empirical testing. It is based on the information theory of Shannon, and treats the mind/brain as an information processing organ that aims to be information-efficient, in that it predicts its world, so as to use information efficiently, and regularly re-represents it, so as to store information efficiently. The theory is presented in context of a background review of various research areas that impinge upon its development. Consequences of the theory and testable hypotheses arising from it are discussed.

Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to ‘action’ that do not cause any overt movement. Indeed for any complex body, human or embodied robot inhabiting unstructured environments, the dual processes of shaping motor output during action execution and providing the self with information related to feasibility, consequence and understanding of potential actions (of oneself/others) must seamlessly alternate during goal-oriented behaviors, social interactions. While prominent approaches like Optimal Control, Active Inference converge on the role of forward models, they diverge on the underlying computational basis. In this context, revisiting older ideas from motor control like the Equilibrium Point Hypothesis and synergy formation, this article offers an alternative perspective emphasizing the functional role of a ‘plastic, configurable’ internal representation of the body (body-schema) as a critical link enabling the seamless continuum between motor control and imagery. With the central proposition that both “real and imagined” actions are consequences of an internal simulation process achieved though passive goal-oriented animation of the body schema, the computational/neural basis of muscleless motor synergies (and ensuing simulated actions without movements) is explored. The rationale behind this perspective is articulated in the context of several interdisciplinary studies in motor neurosciences (for example, intracranial depth recordings from the parietal cortex, FMRI studies highlighting a shared cortical basis for action ‘execution, imagination and understanding’), animal cognition (in particular, tool-use and neuro-rehabilitation experiments, revealing how coordinated tools are incorporated as an extension to the body schema) and pertinent challenges towards building cognitive robots that can seamlessly “act, interact, anticipate and understand” in unstructured natural living spaces.

During the short period of brain development, nature is able to build the only system we know capable of producing cognition, language, creativity, and consciousness. The neocortex – the outermost layer of the mammalian cerebrum – appears to be the biological substrate of these abilities. Its development requires not only the precise placement and wiring of billions of cells, but also the implementation of mechanisms to ensure a viable cognition despite sometimes dramatic perturbations. Today, this remarkably complex organisation is thought to be genetically encoded, and further refined by activity-dependent processes. We propose that mechanical morphogenesis – the capacity of homogeneously growing elastic tissue to produce complex shapes – can also play an important role. Out of homogeneous growth, mechanical morphogenesis can induce the segregation of the neocortex into mechanical and geometric modules – the neocortical folds. Through the feedback of physical forces on developing tissue, these modules can influence the differentiation and wiring of the neocortex, having a causal role on neocortical development, and providing adaptable and robust units for its evolution.

The focus of this study is auditory localization. Based on wave mechanics and structural mechanics, we analyze the sound field encircling external ear and the function of the soft tissues in middle ear respectively. And then, with the help of two rules, some details of the generation of spatial hearing are revealed. For auditory direction perception, three semicircular canals work as the reference coordinate system, and the cues are the direction of the concentrated force acting on tympanic membrane and the synchronous stress state. For the distance perception, because the distance information of the sound source is converted into some comparisons, the accuracy is closely related to the amount of useable sound sources. Therefore, the bad accuracy of the distance perception is inevitable in most cases. When it is necessary, many cues based on experience will be utilized to enhance the accuracy of the distance perception, which brings the diversity to auditory localization. Additionally, our results also can be used to explain some well known acoustic phenomena, such as auditory localization with one ear and the cocktail party effect.

This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.

In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical systems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these computational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of Gödel's proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality; (ii) the potential to access an infinite computational medium; and (iii) the ability to implement negation. The considered adaptations of Gödel's proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability.

The Operational Architectonics (OA) of brain-mind functioning is a theory that unifies brain and mind through nested and dynamic hierarchy of electromagnetic brain fields. Recently, it has been enriched by concepts from physics like time, space, entropy, and self-organized criticality. This review paper advances OA theory further by delving into the foundations of quantum physics and Eastern metaphysics in relation to mind function. We aim to show that the brain-mind OA is the boundary between and integration point of quantum physics and Eastern metaphysics, and that it may inspire building a richer and more inclusive paradigm of the brain-mind relation, where quantum physics and Eastern metaphysics are inherently intertwined.

Human–machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain–computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human–machine interactions.

Brain activity takes place in three spatial-plus time dimensions. This rather obvious claim has been recently questioned by papers that, taking into account the big data outburst and novel available computational tools, are starting to unveil a more intricate state of affairs. Indeed, various brain activities and their correlated mental functions can be assessed in terms of trajectories embedded in phase spaces of dimensions higher than the canonical ones. In this review, I show how further dimensions may not just represent a convenient methodological tool that allows a better mathematical treatment of otherwise elusive cortical activities, but may also reflect genuine functional or anatomical relationships among real nervous functions. I then describe how to extract hidden multidimensional information from real or artificial neurodata series, and make clear how our mind dilutes, rather than concentrates as currently believed, inputs coming from the environment. Finally, I argue that the principle “the higher the dimension, the greater the information” may explain the occurrence of mental activities and elucidate the mechanisms of human diseases associated with dimensionality reduction.

In recent years, both fields of physics and psychology have made important scientific advances. The emergence of new instruments gave rise to a data-driven neuroscience allowing us to learn about the state of the brain supporting known mental functions and conversely. In parallel, the appearance of new mathematics allowed the development of computational models describing fundamental brain functions and implementing them in technological applications. While emphasizing the methodology of physics, the special issue aims to bring together these trends in both the experimental and theoretical sciences in order to explain some of the most basic mental processes such as perception, cognition, emotion, consciousness, and learning. In this editorial, we define unsolved problems for brain and psychological sciences, discuss possible means towards their respective solutions, and outline some collaborative initiatives aiming toward these goals. The following problems are defined in gradual order of difficulty: what are the universal properties of human behavior across conditions and cultures? What have each culture learned over historical times and why should specific elements of knowledge be accumulated over cultural evolution? Can computational psychiatry help predict, understand, and cure mental disorders? What is the function of art and cultural artifacts such as music, fiction, or poetry for the cognitive system? How to explain the relation between first-person subjective experience and third-person objective physiological data? What neural mechanisms operate on which mental content at the highest levels of organization of the hierarchical brain? How do abstract ideas emerge from sensory-motor contingencies and what are the conditions for the birth of a new concept? Could symmetry play a role in psychogenesis and support the emergence of new hierarchical layers in cognition? How can we start addressing the question of meaning scientifically, and what does it entail for the physical sciences?

Physarum polycephalum, a single-celled, multinucleate slime mould, is a seemingly simple organism, yet it exhibits quasi-intelligent behaviour during extension, foraging, and as it adapts to dynamic environments. For these reasons, Physarum is an attractive target for modelling with the underlying goal to uncover the physiological mechanisms behind the exhibited quasi-intelligence and/or to devise novel algorithms for solving complex computational problems. The recent increase in modelling studies on Physarum has prompted us to review the latest developments in this field in the context of modelling and computing alike. Specifically, we cover models based on (i) morphology, (ii) taxis, and (iii) positive feedback dynamics found in top-down and bottom-up modelling techniques. We also survey the application of each of these core features of Physarum to solving difficult computational problems with real-world applications. Finally, we highlight some open problems in the field and present directions for future research.

We look at a recent expansion of Physarum research from inspiring biomimetic algorithms to serving as a model organism in the evolutionary study of perception, memory, learning, and decision making.

Complexity is an indisputable, well-known, and broadly accepted feature of the brain. Despite the apparently obvious and widely-spread consensus on the brain complexity, sprouts of the single neuron revolution emerged in neuroscience in the 1970s. They brought many unexpected discoveries, including grandmother or concept cells and sparse coding of information in the brain. In machine learning for a long time, the famous curse of dimensionality seemed to be an unsolvable problem. Nevertheless, the idea of the blessing of dimensionality becomes gradually more and more popular. Ensembles of non-interacting or weakly interacting simple units prove to be an effective tool for solving essentially multidimensional and apparently incomprehensible problems. This approach is especially useful for one-shot (non-iterative) correction of errors in large legacy artificial intelligence systems and when the complete re-training is impossible or too expensive. These simplicity revolutions in the era of complexity have deep fundamental reasons grounded in geometry of multidimensional data spaces. To explore and understand these reasons we revisit the background ideas of statistical physics. In the course of the 20th century they were developed into the concentration of measure theory. The Gibbs equivalence of ensembles with further generalizations shows that the data in high-dimensional spaces are concentrated near shells of smaller dimension. New stochastic separation theorems reveal the fine structure of the data clouds. We review and analyse biological, physical, and mathematical problems at the core of the fundamental question: how can high-dimensional brain organise reliable and fast learning in high-dimensional world of data by simple tools? To meet this challenge, we outline and setup a framework based on statistical physics of data. Two critical applications are reviewed to exemplify the approach: one-shot correction of errors in intellectual systems and emergence of static and associative memories in ensembles of single neurons. Error correctors should be simple; not damage the existing skills of the system; allow fast non-iterative learning and correction of new mistakes without destroying the previous fixes. All these demands can be satisfied by new tools based on the concentration of measure phenomena and stochastic separation theory. We show how a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content of high-dimensional data, ii) simultaneous separation of several uncorrelated informational items from a large set of stimuli, and iii) dynamic learning of new items by associating them with already “known” ones. These results constitute a basis for organisation of complex memories in ensembles of single neurons.

The paper introduces a new perspective on abstract concepts (e.g. “freedom”) and their associate words representation, the Words As social Tools (WAT) view. Traditional theories conceptualize language as a way to index referents, a shortcut to access meaning, or a way to access meaning through words associations. WAT goes beyond these theories by identifying additional functions of words and language: words are tools helping us to perform actions and change the state of our social environment, and language is a means to improve our thought abilities, to control our behavior and plays a predictive role, helping us to form categories. Most importantly, WAT proposes that language and sociality – along with interoceptive and metacognitive processes – are key for the grounding of abstract concepts (ACs) that are more complex, variable, and more detached from perceptual and motor experience than concrete concepts (CCs). We highlight four tenets of WAT and discuss each of them in light of recent evidence: a. acquisition: compared to concrete concepts, the acquisition of abstract concepts relies more on social and linguistic inputs; b. brain representation: abstract concepts recruit more linguistic and social brain areas; c. mouth activation: due to the relevance of language for representing them, abstract concepts activate more the oral motor system; d. linguistic variability: abstract concepts are more affected by differences between spoken languages. We discuss evidence supporting these four tenets of WAT, and its advantages and limitations compared to other views on abstract concepts. Finally, we outline a conceptual proposal that specifies how internal models supporting the representation and processing of ACs can be grounded on interoceptive, metacognitive, social, and linguistic experience.

Homeostasis is a problem for all living agents. It entails predictively regulating internal states within the bounds compatible with survival in order to maximise fitness. This can be achieved physiologically, through complex hierarchies of autonomic regulation, but it must also be achieved via behavioural control, both reactive and proactive. Here we briefly review some of the major theories of homeostatic control and their historical cognates, addressing how they tackle the optimisation of both physiological and behavioural homeostasis. We start with optimal control approaches, setting up key concepts, exploring their strengths and limitations. We then concentrate on contemporary neurocomputational approaches to homeostatic control. We primarily focus on a branch of reinforcement learning known as homeostatic reinforcement learning (HRL). A central premise of HRL is that reward optimisation is directly coupled to homeostatic control. A central construct in this framework is the drive function which maps from homeostatic state to motivational drive, where reductions in drive are operationally defined as reward values. We explain HRL's main advantages, empirical applications, and conceptual insights. Notably, we show how simple constraints on the drive function can yield a normative account of predictive control, as well as account for phenomena such as satiety, risk aversion, and interactions between competing homeostatic needs. We illustrate how HRL agents can learn to avoid hazardous states without any need to experience them, and how HRL can be applied in clinical domains. Finally, we outline several challenges to HRL, and how survival constraints and active inference models could circumvent these problems.

The approach to the evolution of human culture and mind suggested in this article represents transactional naturalism combined with methodological relationalism. In transactions, the organism changes the environment and vice versa. Transactional naturalism conceptualizes the human mind and awareness in a relational vein, as coevolving with the human socio-cultural niche, much like enactive and extensive theories of mind. The approach is in stark contrast with gene-centered and psychologizing nativist naturalism which views consciousness and language faculty as innate. The origin of the concept of transaction is in classical American pragmatism, in particular in Dewey's theory of action according to which one can learn only in action and in action one cannot but learn. Apprentice learning setup, emphasized by Kim Sterelny in particular, has been pivotal for the development of human community since the Pleistocene era. Coevolving with social institutions, human learning has played an exceptional role in cultural innovations when useful knowing-how and skilled habits have been transferred from generation to generation. Therefore humans deserve the epithet Homo discens, learning man.

We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. Our results indicate new peculiar features of connectomes of higher organisms. We found that the spectral density of adjacency matrices of human connectome has maximal deviation from the one of randomized network, compared to other organisms. Considering the network evolution induced by the preference of 3-cycles formation, we discovered that for macaque and human connectomes the evolution with the conservation of local clusterization is crucial, while for primitive organisms the conservation of averaged clusterization is sufficient. Investigating for the first time the level spacing distribution of the spectrum of human connectome Laplacian matrix, we explicitly demonstrate that the spectral statistics corresponds to the critical regime, which is hybrid of Wigner-Dyson and Poisson distributions. This observation provides strong support for debated statement of the brain criticality.

Cosmic evolution is the tale of progressive transition from simplicity to complexity. The newborn universe started with the simplest atoms formed after the Big Bang and proceeded toward the formation of the so-called ‘astronomical complex organic molecules’ (aCOMs), most of them showing a clear prebiotic character. Understanding the chemical evolution of the universe is one of the main aims of Astrochemistry, with the starting point being the knowledge whether a molecule is present in the astronomical environment under consideration and, if so, its abundance. However, the interpretation of astronomical detections and the identification of molecules are not at all straightforward. Indeed, the extraterrestrial chemical inventory has been obtained by means of astronomical observations based on spectroscopic signatures determined in laboratory (either experimental or computational) studies. Even though the presence of aCOMs has been known for decades, the processes that lead to their synthesis are still hotly debated or even unknown. It is often assumed that aCOMs are mostly synthesized on grain surfaces during the so-called warm-up phase, when various radicals trapped in the grain mantles acquire mobility and recombine into large molecules. However, recent detections of aCOMs in cold environments have challenged this exclusive role of grain-surface chemistry. Clearly, gas-phase chemistry is at work in cold environments. Moving to Titan's atmosphere, prior to the Cassini-Huygens arrival in the Saturn system, it was generally believed that Earth and interstellar space are the two places where organic molecules are/were synthesized extensively. However, the experimental measurements by the instruments on board the Cassini orbiter spacecraft and the Huygens probe lander have changed this view. To disclose the “secrets” of chemical evolution across space, the first step is the understanding of how small prebiotic species are formed and how the chemical complexity can further increase. This review indeed addresses the chemical evolution in space, focusing – in particular – on the role played by molecular spectroscopy and quantum-chemical computations. To summarize, in this review we will first of all present how the signatures of molecules can be found in space. Then, we will address, from a computational point of view, the derivation of the molecular spectroscopic features, the investigation of gas-phase formation routes of prebiotic species in the ISM, and the evolution of chemical complexity, from small molecules to haze, in Titan's atmosphere. Finally, an integrated strategy, also involving high-performance computers and virtual reality, will be discussed.

A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.

Directional hemispheric dominance has been established for numerous cognitive functions in the human brain. Strong population biases with some functions favoring the left and others the right hemisphere generated the popular idea of an advantageous prototypical division of labor between both halves of the brain, molded by evolution and genetically blueprinted. As most empirical studies on functional lateralization focused on a single function at a time, little is known about the relation between different asymmetric functions and the consequences of atypical functional segregation in healthy individuals. Recent investigations suggest the existence of at least three different phenotypes in human functional segregation relevant for future neuroscientific and genetic research. Using atypical language dominance as a starting point, I summarize the existing literature about its behavioral and neural consequences and explore the evidence for intermediate phenotypes in brain functional segregation that could bridge behavioral and genetic data.

Recent advances in molecular biology such as gene editing [1], bioelectric recording and manipulation [2] and live cell microscopy using fluorescent reporters [3], [4] – especially with the advent of light-controlled protein activation through optogenetics [5] – have provided the tools to measure and manipulate molecular signaling pathways with unprecedented spatiotemporal precision. This has produced ever increasing detail about the molecular mechanisms underlying development and regeneration in biological organisms. However, an overarching concept – that can predict the emergence of form and the robust maintenance of complex anatomy – is largely missing in the field. Classic (i.e., dynamic systems and analytical mechanics) approaches such as least action principles are difficult to use when characterizing open, far-from equilibrium systems that predominate in Biology. Similar issues arise in neuroscience when trying to understand neuronal dynamics from first principles. In this (neurobiology) setting, a variational free energy principle has emerged based upon a formulation of self-organization in terms of (active) Bayesian inference. The free energy principle has recently been applied to biological self-organization beyond the neurosciences [6], [7]. For biological processes that underwrite development or regeneration, the Bayesian inference framework treats cells as information processing agents, where the driving force behind morphogenesis is the maximization of a cell's model evidence. This is realized by the appropriate expression of receptors and other signals that correspond to the cell's internal (i.e., generative) model of what type of receptors and other signals it should express. The emerging field of the free energy principle in pattern formation provides an essential quantitative formalism for understanding cellular decision-making in the context of embryogenesis, regeneration, and cancer suppression. In this paper, we derive the mathematics behind Bayesian inference – as understood in this framework – and use simulations to show that the formalism can reproduce experimental, top-down manipulations of complex morphogenesis. First, we illustrate this ‘first principle’ approach to morphogenesis through simulated alterations of anterior-posterior axial polarity (i.e., the induction of two heads or two tails) as in planarian regeneration. Then, we consider aberrant signaling and functional behavior of a single cell within a cellular ensemble – as a first step in carcinogenesis as false ‘beliefs’ about what a cell should ‘sense’ and ‘do’. We further show that simple modifications of the inference process can cause – and rescue – mis-patterning of developmental and regenerative events without changing the implicit generative model of a cell as specified, for example, by its DNA. This formalism offers a new road map for understanding developmental change in evolution and for designing new interventions in regenerative medicine settings.

Neuroscience has made considerable progress in unraveling the neural correlates of mental phenomena like self, consciousness, and perception. However, the “common currency” shared between neuronal and mental activity, brain and mind, remains yet unclear. In this article, we propose that the dynamics of time and space provides a “common currency” that connects neuronal and mental features. Time and space are here understood in a dynamic context (as in contemporary physics): that is, in terms of the way the brain's spontaneous activity constructs its spatial and temporal relationships, for instance in terms of functional connectivity and different frequencies of fluctuations. Recruiting recent empirical evidence, we show that the different ways in which the spontaneous activity constructs its “inner time and space” are manifested in distinct mental features. Specifically, we demonstrate how spatiotemporal mechanisms like spatiotemporal repertoire, integration, and speed yield mental features like consciousness, self, and time speed perception. The focus on the brain's spatiotemporal mechanisms entails what we describe as “Spatiotemporal Neuroscience”. Spatiotemporal Neuroscience conceives neuronal activity in terms of its temporo-spatial dynamics rather than its various functions (e.g., cognitive, affective, social, etc.) as in other branches of neuroscience (as distinguished from Cognitive, Affective, Cultural, Social, etc. Neuroscience). That allows Spatiotemporal Neuroscience to take into view the so-called ‘spatio-temporality’ of mental features including their non-causal, intrinsic and transformative relationship with neuronal features. In conclusion, Spatiotemporal Neuroscience opens the door to investigate and ultimately reveal the brain's own temporo-spatial dynamics as the hitherto missing “common currency” of neuronal and mental features.

Humans are fundamentally defined by our socially transmitted, often long-lived, sophisticated cultural traits. The nature of cultural transmission is the subject of ongoing debate: while some emphasize that it is a biased, transformational process, others point out that high-fidelity transmission is required to explain the quintessentially cumulative nature of human culture. This paper integrates both views into a model that has two main components: First, actions – observable motor-behavioural patterns – are inherited with high fidelity, or replicated, when they are copied, largely independently of their normal, effective or conventional function, by naive learners. Replicative action copying is the unbiased transmission process that ensures the continuity of cultural traditions. Second, mental culture – knowledge, skills, attitudes and values – is not inherited directly or faithfully, but instead emerges, or develops, during usage, when individuals learn the associations between actions and their contexts and outcomes. Mental cultural traits remain stable over generations to the extent that they are informed by similar (replicated) motor patterns unfolding in similar environments. The arguments in support of this model rest on clear distinctions between inheritance and usage; between public-behavioural and private-mental culture; and between selection for fidelity and selection for function.

Extremophilic microbes have adapted to thrive in ecological niches characterized by harsh chemical/physical conditions such as, for example, very low/high temperature. Living organisms inhabiting these environments have developed peculiar mechanisms to cope with extreme conditions, in such a way that they mark the chemical-physical boundaries of life on Earth. Studying such mechanisms is stimulating from a basic research viewpoint and because of biotechnological applications. Pseudoalteromonas species are a group of marine gamma-proteobacteria frequently isolated from a range of extreme environments, including cold habitats and deep-sea sediments. Since deep-sea floors constitute almost 60% of the Earth's surface and cold temperatures represent the most common of the extreme conditions, the genus Pseudoalteromonas can be considered one of the most important model systems for studying microbial adaptation. Particularly, among all Pseudoalteromonas representatives, P. haloplanktis TAC125 has recently gained a central role. This bacterium was isolated from seawater sampled along the Antarctic ice-shell and is considered one of the model organisms of cold-adapted bacteria. It is capable of thriving in a wide temperature range and it has been suggested as an alternative host for the soluble overproduction of heterologous proteins, given its ability to rapidly multiply at low temperatures. In this review, we will present an overview of the recent advances in the characterization of Pseudoalteromonas strains and, more importantly, in the understanding of their evolutionary and chemical-physical strategies to face such a broad array of extreme conditions. A particular attention will be given to systems-biology approaches in the study of the above-mentioned topics, as genome-scale datasets (e.g. genomics, proteomics, phenomics) are beginning to expand for this group of organisms. In this context, a specific section dedicated to P. haloplanktis TAC125 will be presented to address the recent efforts in the elucidation of the metabolic rewiring of the organisms in its natural environment (Antarctica).

There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-2015 Ebola epidemic in West Africa.

We review computational studies on prebiotic evolution, focusing on informatic processes in RNA-like replicator systems. In particular, we consider the following processes: the maintenance of information by replicators with and without interactions, the acquisition of information by replicators having a complex genotype-phenotype map, the generation of information by replicators having a complex genotype-phenotype-interaction map, and the storage of information by replicators serving as dedicated templates. Focusing on these informatic aspects, we review studies on quasi-species, error threshold, RNA-folding genotype-phenotype map, hypercycle, multilevel selection (including spatial self-organization, classical group selection, and compartmentalization), and the origin of DNA-like replicators. In conclusion, we pose a future question for theoretical studies on the origin of life.