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Simulating development has a number of benefits for understanding the acquisition of intelligent behavior. Yet development is not simply a generic form of generativity or emergence. Development is an inherently embodied and interactive process that unfolds in a limited time span. While embodied development provides a basis for grounding intelligent behavior, it also serves as a means to differentiate various behaviors with respect to the origins of phenotypic characteristics. Agentive development requires both morphogenetic and behavioral acquisitions which can be dependent upon one another. On the other hand, development requires innate and invariant features which interact with but are distinct from the environment. We propose a set of models that define critical period acquisition in an embodied developmental context. Critical periods are periods of enhanced acquisition that shape future learning and experience. Leading to contingencies that affect the ability of an agent to integrate environmental information, critical periods also require the acquisition of the phenotype itself. leading to contingencies that affect the ability of an agent to integrate environmental information. This work is presented in light of the Developmental Neurosimulation paradigm, in addition to understanding agentive development from a biologically-inspired perspective.

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.

Cognitive offloading occurs when environmental affordances expand cognitive capacity while facilitating spatial and social behaviors. Capacity-related constraints are also important, particularly as embodied agents come online during development. Vast differences in brain size and offloading capacity exist across the tree of life. We take from multiple perspectives to understand the proportional contributions of internal models (brain) and externalized processing (offloading) in developing embodied computational agents. As developing nervous systems scale with body size and/or functional importance, offloading is also driven by neural capacity. Cognitive capacity is ultimately determined by various innate and environmental constraints. We propose a similar model for computationally developing cognitive agents. A regulatory model of cognition is proposed as a means to build cognitive systems that interface with biologically-inspired substrates. Multiple tradeoffs result from energetic, innate, and informational constraints, and determine the proportion of internal to external information processing capacity. As growth of a biologically-inspired substrate accelerates or decelerates over developmental time, it changes the acquisitional capacity of the agent. Our agent’s capacity limitations determine externalization potential, which is characterized by three parameters and two mathematical functions. The neurosimulation approach to intelligence offloading can be applied to a broad range of agent-based models and Artificial Intelligences.

Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allow for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.

Biological development is often described as a dynamic, emergent process. Yet beyond the observation of gene expression in individual cells, it is hard to conceptualize large-scale patterns that confirm this description. We provide an example of combining theoretical insights with a data science approach. The availability of quantitative data allows us to examine aggregate trends across development, from the spatial organization of embryo cells to the temporal trends as they differentiate. The first half of this paper lays out alternatives to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Caenorhabditis elegans biology provides us with a highly-deterministic developmental cell lineage and clear linkage between zygote and cells of the adult phenotype. These properties allow us to examine time-dependent properties of the embryonic phenotype. We utilize the unique life-history properties of C. elegans to demonstrate how these emergent properties can be linked together by relational processes and data analysis. The second half of this paper focuses on the process of developmental cell terminal differentiation, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. An analysis is conducted for cells that were present during discrete time intervals covering 200 to 400 minutes of embryogenesis, providing us with basic statistics on the tempo of the process in addition to the appearance of specific cell types and their order relative to developmental time. As with ideas presented in the first section, these data may also provide clues as to the timing for the initial onset of stereotyped and autonomic behaviors of the developing animal. Taken together, these overlapping approaches can provide critical links across life-history, anatomy and function.

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

We propose a new way to quantitatively characterize information: Gibsonian Information (GI). This framework is relevant to both the study of cognitive agents and single cell systems that exhibit cognitive behaviors. GI provides a means to characterize how agents extract information from direct perceptual signals. This differs from existing information theories in two ways. The first involves an emphasis on sensory processing, engagement in collective behaviors, and the dynamic evolution of such interactions. GI is useful for understanding first-order sensory inputs in terms of agent interactions with naturalistic contexts and higher-order representations. This allows us to extend GI to cybernetic and other types of symbolic systems representations. GI also emphasizes the role of information content in the relationship between ecology and nervous systems. Along with direct sensory input and simple internal representations, statistical affordances (clustered information that is spatiotemporally dependent perceptual input) facilitate extraction of GI from the environment. As a quantitative accounting of perceptual information, GI provides a means to measure a generalized indicator of nervous system input, and can be characterized by three scenarios: disjoint distributions, contingent action, and coherent movement. All of these cases provide a means to create a differential system between both motion (information) and random noise/stasis (non-information). By applying this framework to a variety of specific contexts, including a four-channel model of multisensory embodiment, we demonstrate how GI is essential to understanding the full scope of cognitive information processing.

Open and reproducible research practices increase the reusability and impact of scientific research. The reproducibility of research results is influenced by many factors, most of which can be addressed by improved education and training. Here we describe how workshops developed by the Reproducibility for Everyone (R4E) initiative can be customized to provide researchers at all career stages and across most disciplines with education and training in reproducible research practices. The R4E initiative, which is led by volunteers, has reached more than 3000 researchers worldwide to date, and all workshop materials, including accompanying resources, are available under a CC-BY 4.0 license at https://www.repro4everyone.org/.

We can improve our understanding of biological processes through the use of computational and mathematical modeling. One such morphogenetic process (ommatidia formation in theDrosophilaeye 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 three 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.

Synopsis 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.

In the present paper we will approach enactivism from the perspective of internal regulation: while the environment shapes the organism, it is also true that organisms have complex internal states with regulatory machinery with a set of continuous phenotype-environment interactions. The aim of the present paper is to provide a visual means to analyze these interactions in individuals and computational agents alike. An essential component of our approach is the representation of continuous internal states through the usage of the single continuous indicator we call an Allostasis Machine (AM). Consequently, we consider potential perturbation regimes for both naturalistic and virtual environments: within the naturalistic cases, it is possible to observe the effects of perturbations in isolation, or as overlapping, multiplicative events. In virtual cases, we can observe perturbations as the outcome of both realistic and fantastical environments. To conclude, we discuss how AMs can be utilized to improve our understanding of both the theoretical basis of embodied interaction and the dynamic regulation of complex psychophysiological states.

Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

As a biochemical process, direct cellular reprogramming is slow and complex. The early stages of this process is the most critical determinant of successful phenotypic conversion. This study provides insight into the statistical signatures that describe temporal structure in the reprogramming process. We examine two sources of variation in reprogramming cells: clonal instances from various tissues of origin and rate of expansion between these lines. Our analytical strategy involved modeling the potential of populations to reprogram, and then applying statistical models to capture this potential in action. This two-fold approach utilizes both conventional and novel techniques that allow us to infer and confirm a host of properties that define the phenomenon. These results can be summarized in a number of ways, and essentially suggest that reprogramming is organized around changes in gene expression phenotype (phases) which happens sporadically across a cellular population (bursts).

Literary artefacts are generally indexed and searched based on titles, meta data and keywords over the years. This searching and indexing works well when user/reader already knows about that particular creative textual artefact or document. This indexing and search hardly takes into account interest and emotional makeup of readers and its mapping to books. In case of literary artefacts, progression of emotions across the key events could prove to be the key for indexing and searching. In this paper, we establish clusters among literary artefacts based on computational relationships among sentiment progressions using intelligent text analysis. We have created a database of 1076 English titles + 20 Marathi titles and also used database http://www.cs.cmu.edu/~dbamman/booksummaries.html with 16559 titles and their summaries. We have proposed Sentiment Progression based Search and Indexing (SPbSI) for locating and recommending books. This can be used to create personalized clusters of book titles of interest to readers. The analysis clearly suggests better searching and indexing when we are targeting book lovers looking for a particular type of books or creative artefacts.

Reproducibility is a cornerstone of the scientific method and sets apart science from pseudoscience. Unfortunately, a majority of scientists have experienced difficulties in reproducing their own or someone else’s results. This inability to confirm scientific findings negatively impacts individual scientists, funding bodies, academic journals, pharmaceutical drug development and the public’s perception of science. Factors causing irreproducible results can arise from nearly every aspect of the scientific process, and typically reflect a lack of in-depth training in reproducible research practices. Here, we present the Reproducibility for Everyone (R4E) initiative, a collaboration between researchers from diverse scientific disciplines and industry partners who aspire to promote open and reproducible research practices. We have developed a customizable workshop series targeting researchers at all levels and across disciplines. Our workshop series covers the conceptual framework of reproducible research practices followed by an overview of actionable research practices. To date, we have reached more than 2000 researchers through over 25 workshops held at international conferences and local meetings. By incorporating further contributions from the scientific community, we hope to expand this valuable resource for teaching transparent and reproducible research practices. Our initiative demonstrates how a shared set of materials may form the basis for a global initiative to improve reproducibility in science. The workshop materials, including accompanying resources, are available under a CC-BY 4.0 license at www.repro4everyone.org.