Embodied cognitive morphogenesis as a route to intelligent systems.

The embryological view of development is that coordinated gene expression, cellular physics and migration provides the basis for phenotypic complexity. This stands in contrast with the prevailing view of embodied cognition, which claims that informational feedback between organisms and their environment is key to the emergence of intelligent behaviours. We aim to unite these two perspectives as embodied cognitive morphogenesis, in which morphogenetic symmetry breaking produces specialized organismal subsystems which serve as a substrate for the emergence of autonomous behaviours. As embodied cognitive morphogenesis produces fluctuating phenotypic asymmetry and the emergence of information processing subsystems, we observe three distinct properties: acquisition, generativity and transformation. Using a generic organismal agent, such properties are captured through models such as tensegrity networks, differentiation trees and embodied hypernetworks, providing a means to identify the context of various symmetry-breaking events in developmental time. Related concepts that help us define this phenotype further include concepts such as modularity, homeostasis and 4E (embodied, enactive, embedded and extended) cognition. We conclude by considering these autonomous developmental systems as a process called connectogenesis, connecting various parts of the emerged phenotype into an approach useful for the analysis of organisms and the design of bioinspired computational agents.

Braitenberg Vehicles as Developmental Neurosimulation.

Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.

Data-Theoretical Synthesis of the Early Developmental Process.

Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model system Caenorhabditis elegans, we examine time-dependent properties of the embryonic phenotype and utilize the unique life-history properties to demonstrate how these emergent properties can be linked together by data analysis and theory-building. We also focus on embryogenetic differentiation processes, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. Examining embryogenetic dynamics from 200 to 400 min post-fertilization provides basic quantitative information on developmental tempo and process. To summarize, theory construction techniques are summarized and proposed as a way to rigorously interpret our data. Our proposed approach to a formal data representation that can provide critical links across life-history, anatomy and function.

Complex Temporal Biology: Towards a Unified Multi-Scale Approach to Predict the Flow of Information.

Two hallmarks of biological processes are complexity and time. While complexity can have many meanings, in this paper we propose an explicit link to the flow of time and how it is experienced by the organism. While the flow of time is rooted in constraints of fundamental physics, understanding the operation of biological systems in terms of processual flow and tempo is more elusive. Fortunately, the convergence of new computational and methodological perspectives will provide a means to transform complicated, nonlinear paths between related phenomena at different time scales into dynamic four-dimensional perspectives. According to the complex temporal biology approach, information flow between time scales of multiple lengths is a transformational process that acts to regulate life's complexity. Interactions between temporal intervals of differing magnitude and otherwise loosely-related mechanisms can be understood as inter-timescale information flow. We further propose that informational flow between time scales is the glue that binds the multiple vertical layers of biocomplexity, as well as yielding surprising outcomes ranging from complex behaviors to the persistence of lineages. Building a foundation of rules based on common interactions between orders of time and common experiential contexts would help to reintegrate biology. Emerging methodologies such as state-of-the-art imaging, visualization techniques, and computational data analysis can help us uncover these interactions. In conclusion, we propose educational and community-level changes that would better enable our vision.

Periodicity in the embryo: Emergence of order in space, diffusion of order in time.

Does embryonic development exhibit characteristic temporal features? This is apparent in evolution, where evolutionary change has been shown to occur in bursts of activity. Using two animal models (Nematode, Caenorhabditis elegans and Zebrafish, Danio rerio) and simulated data, we demonstrate that temporal heterogeneity exists in embryogenesis at the cellular level, and may have functional consequences. Cell proliferation and division from cell tracking data is subject to analysis to characterize specific features in each model species. Simulated data is then used to understand what role this variation might play in producing phenotypic variation in the adult phenotype. This goes beyond a molecular characterization of developmental regulation to provide a quantitative result at the phenotypic scale of complexity.

Creating clear and informative image-based figures for scientific publications.

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

Raising the Connectome: The Emergence of Neuronal Activity and Behavior in Caenorhabditis elegans.

The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of Caenorhabditis elegans will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.

Morphogenetic processes as data: Quantitative structure in the Drosophila eye imaginal disc.

We can improve our understanding of biological processes through the use of computational and mathematical modeling. One such morphogenetic process (ommatidia formation in the Drosophila eye imaginal disc) provides us with an opportunity to demonstrate the power of this approach. We use a high-resolution image that catches the spatially- and temporally-dependent process of ommatidia formation in the act. This image is converted to quantitative measures and models that provide us with new information about the dynamics and geometry of this process. We approach this by addressing four computational hypotheses, and provide a publicly-available repository containing data and images for further analysis. Potential spatial patterns in the morphogenetic furrow and ommatidia are summarized, while the ommatidia cells are projected to a spherical map in order to identify higher-level spatiotemporal features. In the conclusion, we discuss the implications of our approach and findings for developmental complexity and biological theory.

Cell differentiation processes as spatial networks: Identifying four-dimensional structure in embryogenesis.

One overarching principle of eukaroytic development is the generative spatial emergence and self-organization of cell populations. As cells divide and differentiate, they and their descendents form a spatiotemporal explicit and increasingly compartmentalized complex system. Yet despite this comparmentalization, there is selective functional overlap between these structural components. While contemporary tools such as lineage trees and molecular signaling networks prvide a window into this complexity, they do not characterize embryogenesis as a global process. Using a four-dimensional spatial representation, major features of the developmental process are revealed. To establish the role of developmental mechanisms that turn a spherical embryo into a highly asymmetrical adult phenotype, we can map the outcomes of the cell division process to a complex network model. This representational model provides information about the top-down mechanisms relevant to the differentiation process. In a complementary manner, looking for phenomena such as superdiffusive positioning and sublineage-based anatomical clustering incorporates dynamic information to our parallel view of embryogenesis. Characterizing the spatial organization and geometry of embryos in this way allows for novel indicators of developmental patterns both within and between organisms.

The emergent connectome in Caenorhabditis elegans embryogenesis.

The relatively new field of connectomics provides us with a unique window into nervous system function. In the model organism Caenorhabditis elegans, this promise is even greater due to the relatively small number of cells (302) in its nervous system. While the adult C. elegans connectome has been characterized, the emergence of these networks in development has yet to be established. In this paper, we approach this problem using secondary data describing the birth times of terminally-differentiated cells as they appear in the embryo and a connectomics model for nervous system cells in the adult hermaphrodite. By combining these two sources of data, we can better understand patterns that emerge in an incipient connectome. This includes identifying at what point in embryogenesis the cells of a connectome first comes into being, potentially observing some of the earliest neuron-neuron interactions, and making comparisons between the formally-defined connectome and developmental cell lineages. An analysis is also conducted to root terminally-differentiated cells in their developmental cell lineage precursors. This reveals subnetworks with different properties at 300 min of embryogenesis. Additional investigations reveal the spatial position of neuronal cells born during pre-hatch development, both within and outside the connectome model, in the context of all developmental cells in the embryo. Overall, these analyses reveal important information about the birth order of specific cells in the connectome, key building blocks of global connectivity, and how these structures correspond to key events in early development.

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans.

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Origins of the Embryo: Self-organization through cybernetic regulation.

The construction of an embryo from a single cell precursor is a highly complex process. Evolutionary emergence of the first embryos is even more complex, and involves both a transition to multicellularity along with the establishment of developmental mechanisms. We propose that embryogenesis relies on a community of cells conforming to a regulatory model of emergent multicellularity. This model draws together multiple threads in the scientific literature, from complexity theory to cybernetics, and from thermodynamic entropy to artificial life. All of these strands come together to inform a model of goal-oriented regulation for emergent structures in early life. This is an important step in the evolution of early life, as well as the emergence of complex life in the earliest habitats. Our model, called the cybernetic embryo, allows for a systems-level view of the embryogenetic process.

Network Dynamics of Attention During a Naturalistic Behavioral Paradigm.

This study investigates the dynamics of attention during continuous, naturalistic interactions in a video game. Specifically, the effect of repeated distraction on a continuous primary task is related to a functional model of network connectivity. We introduce the Non-linear Attentional Saturation Hypothesis (NASH), which predicts that effective connectivity within attentional networks increases non-linearly with decreasing distraction over time, and exhibits dampening at critical parameter values. Functional magnetic resonance imaging (fMRI) data collected using a naturalistic behavioral paradigm coupled with an interactive video game is used to test the hypothesis. As predicted, connectivity in pre-defined regions corresponding to attentional networks increases as distraction decreases. Moreover, the functional relationship between connectivity and distraction is convex, that is, network connectivity somewhat increases as distraction decreases during the continuous primary task, however, connectivity increases considerably as distraction falls below critical levels. This result characterizes the non-linear pattern of connectivity within attentional networks, particularly with respect to their dynamics during behavior. These results are also summarized in the form of a network structure analysis, which underscores the role of various nodes in regulating the global network state. In conclusion, we situate the implications of this research in the context of cognitive complexity and an emerging theory of flow during media exposure.

The long road to theoretical synthesis. A review of Embryogenesis Explained written by Natalie K. Gordon and Richard Gordon.

Review of: Embryogenesis Explained written by Natalie K. Gordon and Richard Gordon.

Evolution in eggs and phases: experimental evolution of fecundity and reproductive timing in Caenorhabditis elegans.

To examine the role of natural selection in fecundity in a variety of Caenorhabditis elegans genetic backgrounds, we used an experimental evolution protocol to evolve 14 distinct genetic strains over 15-20 generations. We were able to generate 790 distinct genealogies, which provided information on both the effects of natural selection and the evolvability of each strain. Among these genotypes are a wild-type (N2) and a collection of mutants with targeted mutations in the daf-c, daf-d and AMPK pathways. Differences are observed in reproductive fitness along with related changes in reproductive timing. The majority of selective effects on fecundity occur during the first few generations of evolution, while the negative selection for reproductive timing occurs on longer time scales. In addition, positive selection on fecundity results in positive and negative strain-dependent selection on reproductive timing. A derivative of population size per generation called reproductive carry-over (RCO) may be informative in terms of developmental selection. While these findings transcend mutations in a specific gene, changes in the RCO measure may nevertheless be products of selection. In conclusion, the broader implications of these findings are discussed, particularly in the context of genotype-fitness maps and the role of uncharacterized mutations in individual variation and evolvability.

Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development via Differentiation Trees of Embryos.

Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.

Toy models for macroevolutionary patterns and trends.

Many models have been used to simplify and operationalize the subtle but complex mechanisms of biological evolution. Toy models are gross simplifications that nevertheless attempt to retain major essential features of evolution, bridging the gap between empirical reality and formal theoretical understanding. In this paper, we examine thirteen models which describe evolution that also qualify as such toy models, including the tree of life, branching processes, adaptive ratchets, fitness landscapes, and the role of nonlinear avalanches in evolutionary dynamics. Such toy models are intended to capture features such as evolutionary trends, coupled evolutionary dynamics of phenotype and genotype, adaptive change, branching, and evolutionary transience. The models discussed herein are applied to specific evolutionary contexts in various ways that simplify the complexity inherent in evolving populations. While toy models are overly simplistic, they also provide sufficient dynamics for capturing the fundamental mechanism(s) of evolution. Toy models might also be used to aid in high-throughput data analysis and the understanding of cultural evolutionary trends. This paper should serve as an introductory guide to the toy modeling of evolutionary complexity.

Defining the diversity of phenotypic respecification using multiple cell lines and reprogramming regimens.

To better understand the basis of variation in cellular reprogramming, we performed experiments with two primary objectives: first, to determine the degree of difference, if any, in reprogramming efficiency among cells lines of a similar type after accounting for technical variables, and second, to compare the efficiency of conversion of multiple similar cell lines to two separate reprogramming regimens-induced neurons and induced skeletal muscle. Using two reprogramming regimens, it could be determined whether converted cells are likely derived from a distinct subpopulation that is generally susceptible to reprogramming or are derived from cells with an independent capacity for respecification to a given phenotype. Our results indicated that when technical components of the reprogramming regimen were accounted for, reprogramming efficiency was reproducible within a given primary fibroblast line but varied dramatically between lines. The disparity in reprogramming efficiency between lines was of sufficient magnitude to account for some discrepancies in published results. We also found that the efficiency of conversion to one phenotype was not predictive of reprogramming to the alternate phenotype, suggesting that the capacity for reprogramming does not arise from a specific subpopulation with a generally "weak grip" on cellular identity. Our findings suggest that parallel testing of multiple cell lines from several sources may be needed to accurately assess the efficiency of direct reprogramming procedures, and that testing a larger number of fibroblast lines--even lines with similar origins--is likely the most direct means of improving reprogramming efficiency.

Virtual reality in neuroscience research and therapy.

Virtual reality (VR) environments are increasingly being used by neuroscientists to simulate natural events and social interactions. VR creates interactive, multimodal sensory stimuli that offer unique advantages over other approaches to neuroscientific research and applications. VR's compatibility with imaging technologies such as functional MRI allows researchers to present multimodal stimuli with a high degree of ecological validity and control while recording changes in brain activity. Therapists, too, stand to gain from progress in VR technology, which provides a high degree of control over the therapeutic experience. Here we review the latest advances in VR technology and its applications in neuroscience research.