Revealing gene expression heterogeneity in a clonal population of Tetrahymena thermophila through single-cell RNA sequencing.

We performed single-cell RNA sequencing (scRNA-seq) on a population of 5,000 Tetrahymena thermophila, using the 10x Genomics 3' gene expression analysis, to investigate gene expression variability within this clonal population. Initially, we estimated the 3'-untranslated regions (3' UTRs), which were absent in existing annotation files but are crucial for the 10x Genomics 3' gene expression analysis, using the peaks2utr method. This allowed us to create a modified annotation file, which was then utilized in our scRNA-seq analysis. Our analysis revealed significant gene expression variability within the population, even after removing the effect of cell phase-related features. This variability predominantly appeared in six distinct clusters. Through gene ontology and KEGG pathway enrichment analyses, we identified that these were primarily associated with ribosomal proteins, proteins specific to mitochondria, proteins involved in peroxisome-specific carbon metabolism, cytoskeletal proteins, motor proteins, and immobilized antigens.

Spiking neural networks produce informational closure by stimulus avoidance.

The concept of Learning by Stimulus Avoidance (LSA) has been proposed in recent literature, and the methods of avoiding stimuli: action, prediction, and separation appear to align well with the formation of Bertschinger's informational closure. In this study, we provide experimental evidence demonstrating that spiking neural networks, which avoid stimuli, can indeed facilitate the emergence of informational closure. The established link between LSA and informational closure lays the foundation for further exploration of autopoietic relationships and the self-organization of closure within neural networks.

Spontaneous and information-induced bursting activities in honeybee hives.

Social entrainment is important for functioning of beehive organization. By analyzing a dataset of approximately 1000 honeybees (Apis mellifera) tracked in 5 trials, we discovered that honeybees exhibit synchronized activity (bursting behavior) in their locomotion. These bursts occurred spontaneously, potentially as a result of intrinsic bee interactions. The empirical data and simulations demonstrate that physical contact is one of the mechanisms for these bursts. We found that a subset of honeybees within a hive which become active before the peak of each burst, and we refer to these bees as "pioneer bees." Pioneer bees are not selected randomly, but rather, are linked to foraging behavior and waggle dancing, which may help spread external information in the hive. By using transfer entropy, we found that information flows from pioneer bees to non-pioneer bees, which suggest that the bursting behavior is caused by foraging behavior and spreading the information through the hive and promoting integrated group behavior among individuals.

From autopoiesis to self-optimization: Toward an enactive model of biological regulation.

The theory of autopoiesis has been influential in many areas of theoretical biology, especially in the fields of artificial life and origins of life. However, it has not managed to productively connect with mainstream biology, partly for theoretical reasons, but arguably mainly because deriving specific working hypotheses has been challenging. The theory has recently undergone significant conceptual development in the enactive approach to life and mind. Hidden complexity in the original conception of autopoiesis has been explicated in the service of other operationalizable concepts related to self-individuation: precariousness, adaptivity, and agency. Here we advance these developments by highlighting the interplay of these concepts with considerations from thermodynamics: reversibility, irreversibility, and path-dependence. We interpret this interplay in terms of the self-optimization model, and present modeling results that illustrate how these minimal conditions enable a system to re-organize itself such that it tends toward coordinated constraint satisfaction at the system level. Although the model is still very abstract, these results point in a direction where the enactive approach could productively connect with cell biology.

Organization of a Latent Space structure in VAE/GAN trained by navigation data.

We present a novel artificial cognitive mapping system using generative deep neural networks, called variational autoencoder/generative adversarial network (VAE/GAN), which can map input images to latent vectors and generate temporal sequences internally. The results show that the distance of the predicted image is reflected in the distance of the corresponding latent vector after training. This indicates that the latent space is self-organized to reflect the proximity structure of the dataset and may provide a mechanism through which many aspects of cognition are spatially represented. The present study allows the network to internally generate temporal sequences that are analogous to the hippocampal replay/pre-play ability, where VAE produces only near-accurate replays of past experiences, but by introducing GANs, the generated sequences are coupled with instability and novelty.

Analysis and Design of Social Presence in a Computer-Mediated Communication System.

Social presence, or the subjective experience of being present with another existing person, varies with the interaction medium. In general, social presence research has mainly focused on uni-directional aspects of each exchanged message, not on bidirectional interactions. Our primary purpose is to introduce such bidirectional evaluation by quantifying the degree of social presence with a few statistical measures. To this end, we developed a software called "TypeTrace" that records all keystrokes of online chat interactants and reenacts their typing actions and analyzed the results from different chat conditions, mainly focusing on the characterization of bi-directional interactions. We also compared the chat interaction patterns with the patterns from phone call datasets to investigate the difference of live communication in different media. The hypothesis of the experiment was that either richness or concurrency of communication is important for organizing social presence. Richness is defined by the variety of information at a time in communication and the concurrency is the number of temporal thread being processed at the same time. Our results show that when we merely increase the richness of information by presenting the typing process, the cognition of others' presence does not significantly increase. However, when the information concurrency is augmented by introducing the transmission of realtime text, we found that the transfer entropy between the interactants becomes considerably higher, and the social presence and emotional arousal, intimacy increased. High transfer entropy was also observed in the phone call dataset. This result shows that the mere augmentation of information richness does not necessarily lead to increased social presence, and concurrent communication is another critical factor for fostering vivid conversation in digital environments.

Spatial and temporal Taylor's law in 1D chaotic maps.

By using low-dimensional chaotic maps, the power-law relationship established between the sample mean and variance called Taylor's Law (TL) is studied. In particular, we aim to clarify the relationship between TL from the spatial ensemble (STL) and the temporal ensemble (TTL). Since the spatial ensemble corresponds to independent sampling from a stationary distribution, we confirm that STL is explained by the skewness of the distribution. The difference between TTL and STL is shown to be originated in the temporal correlation of a dynamics. In case of logistic and tent maps, the quadratic relationship in the sample mean and variance, called Bartlett's law, is found analytically. On the other hand, TTL in the Hassell model can be well explained by the chunk structure of the trajectory, whereas the TTL of the Ricker model has a different mechanism originated from the specific form of the map.

Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks.

Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.

Personogenesis Through Imitating Human Behavior in a Humanoid Robot "Alter3".

In this study, we report the investigations conducted on the mimetic behavior of a new humanoid robot called Alter3. Alter3 autonomously imitates the motions of a person in front of it and stores the motion sequences in its memory. Alter3 also uses a self-simulator to simulate its own motions before executing them and generates a self-image. If the visual perception (of a person's motion being imitated) and the imitating self-image differ significantly, Alter3 retrieves a motion sequence closer to the target motion from its memory and executes it. We investigate how this mimetic behavior develops interacting with human, by analyzing memory dynamics and information flow between Alter3 and a interacting person. One important observation from this study is that when Alter3 fails to imitate a person's motion, the person tend to imitate Alter3 instead. This tendency is quantified by the alternation of the direction of information flow. This spontaneous role-switching behavior between a human and Alter3 is a way to initiate personality formation (i.e., personogenesis) in Alter3.

An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles.

We propose an approach to open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of time scales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided into three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.

Open-Ended Evolution and a Mechanism of Novelties in Web Services.

Web services are analogous to living ecosystems in nature, in that they form an artificial ecosystem consisting of many tags and their associated media, such as photographs, movies, and web pages created by human users. In biological ecosystems, we view a tag as a species and a human as a hidden environmental resource. Our study examines the evolution of web services, in particular social tagging systems, with respect to the self-organization of new tags. The evolution of new combinations of tags is analyzed as an open-ended evolution (OEE) index. Tag meaning is computed by types of associated tags, including tags that vary their meanings temporally, which, we argue, are examples of OEE.

Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution I Special Issue.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the first of two special issues on current research on OEE and on the more general concept of open-endedness. Most of the papers presented in these special issues are elaborations of work presented at the Third Workshop on Open-Ended Evolution, held in Tokyo as part of the 2018 Conference on Artificial Life.

Reactive, Proactive, and Inductive Agents: An Evolutionary Path for Biological and Artificial Spiking Networks.

Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.

A Sensorimotor Signature of the Transition to Conscious Social Perception: Co-regulation of Active and Passive Touch.

It is not yet well understood how we become conscious of the presence of other people as being other subjects in their own right. Developmental and phenomenological approaches are converging on a relational hypothesis: my perception of a "you" is primarily constituted by another subject's attention being directed toward "me." This is particularly the case when my body is being physically explored in an intentional manner. We set out to characterize the sensorimotor signature of the transition to being aware of the other by re-analyzing time series of embodied interactions between pairs of adults (recorded during a "perceptual crossing" experiment). Measures of turn-taking and movement synchrony were used to quantify social coordination, and transfer entropy was used to quantify direction of influence. We found that the transition leading to one's conscious perception of the other's presence was indeed characterized by a significant increase in one's passive reception of the other's tactile stimulations. Unexpectedly, one's clear experience of such passive touch was consistently followed by a switch to active touching of the other, while the other correspondingly became more passive, which suggests that this intersubjective experience was reciprocally co-regulated by both participants.

Life as an emergent phenomenon: studies from a large-scale boid simulation and web data.

A large group with a special structure can become the mother of emergence. We discuss this hypothesis in relation to large-scale boid simulations and web data. In the boid swarm simulations, the nucleation, organization and collapse dynamics were found to be more diverse in larger flocks than in smaller flocks. In the second analysis, large web data, consisting of shared photos with descriptive tags, tended to group together users with similar tendencies, allowing the network to develop a core-periphery structure. We show that the generation rate of novel tags and their usage frequencies are high in the higher-order cliques. In this case, novelty is not considered to arise randomly; rather, it is generated as a result of a large and structured network. We contextualize these results in terms of adjacent possible theory and as a new way to understand collective intelligence. We argue that excessive information and material flow can become a source of innovation.This article is part of the themed issue 'Reconceptualizing the origins of life'.

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

Open-Ended Evolution: Perspectives from the OEE Workshop in York.

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE-it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .