Maturana and Varela’s notion of autopoiesis has the potential to transform the conceptual foundation of biology as well as the cognitive, behavioral, and brain sciences. In order to fully realize this potential, however, the concept of autopoiesis and its many consequences require significant further theoretical and empirical development. A crucial step in this direction is the formulation and analysis of models of autopoietic systems. This article sketches the beginnings of such a project by examining a glider from Conway’s game of life in autopoietic terms. Such analyses can clarify some of the key ideas underlying autopoiesis and draw attention to some of the central open issues. This article also examines the relationship between an autopoietic perspective on cognition and recent work on dynamical approaches to the behavior and cognition of situated, embodied agents. Relevance: The article focuses on the theory of autopoiesis and related concepts such as structural coupling and cognitive domain.

Maturana and Varela’s concept of autopoiesis defines the essential organization of living systems and serves as a foundation for their biology of cognition and the enactive approach to cognitive science. As an initial step toward a more formal analysis of autopoiesis, this paper investigates its application to the compact, recurrent spatiotemporal patterns that arise in Conway’s Game of Life cellular automata. In particular, we demonstrate how such entities can be formulated as self-constructing networks of interdependent processes that maintain their own boundaries. We then characterize the specific organizations of several such entities, suggest a way to simplify the descriptions of these organizations, and briefly consider the transformation of such organizations over time. Relevance: The paper presents an analysis of a minimal concrete model of autopoiesis to provide a more rigorous foundation for the concept of autopoiesis and highlight its ambiguities and difficulties.

This article revisits the concept of autopoiesis and examines its relation to cognition and life. We present a mathematical model of a 3D tesselation automaton, considered as a minimal example of autopoiesis. This leads us to a thesis T1: “An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopoietic domain.” We propose a modified definition of autopoiesis: “An autopoietic system is a network of processes that produces the components that reproduce the network, and that also regulates the boundary conditions necessary for its ongoing existence as a network.” We also propose a definition of cognition: “A system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint.” It follows from these definitions that the concepts of autopoiesis and cognition, although deeply related in their connection with the regulation of the boundary conditions of the system, are not immediately identical: a system can be autopoietic without being cognitive, and cognitive without being autopoietic. Finally, we propose a thesis T2: “A system that is both autopoietic and cognitive is a living system.”

Key words: Autopoiesis, cognition, life, tessela- tion automaton, random dynamical system, boundary conditions

Excerpt: There has been a long-standing debate – from Leibnitz to Lady Lovelace to the present – over whether purely computational devices are capable of fundamentally-creative, truly emergent behavior. This paper will discuss various kinds of devices capable of emergent behaviors and take up the question of whether we can by purely computational means amplify our capacities as observers and actors in the physical world.

In this paper we present a new model for the mechanism underlying what is traditionally known in immunology as the “selfnonself” distinction. It turns out that in operational terms, the distinction effected by this model of the immune system is between a sufficiently numerous set of antigens present from the start of the ontogeny of the system on the one hand, and isolated antigens first introduced after the system has reached maturity on the other. The coincidence between this “founder versus late” distinction and the traditional “somatic self-foreign pathogen” one is essentially contingent, an example of the purely opportunistic tinkering characteristic of biological organization in general. We conclude that the so-called “self-nonself” distinction in immunology is a misleading misnomer. This raises the question as to what would genuinely count as a “self-nonself” distinction, a fundamental question for biology in general and Artificial Life in particular.

Excerpt: I claim that a mechanism that reconstructs and re-coordinates processes, rather than stores and retrieves labeled descriptions or procedures, is more consistent with what we know about human memory and perception (Clancey 1991, 1994). Such a process memory possibly cannot be built today, because we don’t know how to build the kind of self-organizing mechanism that is required (cf. Freeman 1991). But articulating how human cognition is different from a classical architecture helps delineate what aspects of situated robotic designs are still cast in the classical mold and remain to be freed of prevailing assumptions about the nature of memory and representations.

A novel scenario is emerging from the synthetic biology advancements of the last fifteen years. We refer to a well-defined multidisciplinary sci-tech arena dedicated to the construction of biological-like systems, and, in particular, microscopic cell-like systems. The challenge of assembling a minimal cell from separated parts is generally considered the Holy Grail of biology. However, an accurate analysis of this emerging line of research, grounded in the theory of autopoiesis and its implications, is able to show its potentially high relevance for two other fields – artificial life and artificial intelligence. In this paper we intend to propose this perspective. Based on the critical discussion of recent trends and experimental results in synthetic biology, we sketch out how current research in this field can impact not only artificial life, but also artificial intelligence inquiries, in particular with respect to embodied cognition.

This paper presents a behavioral ontogeny for artificial agents based on the interactive memorization of sensorimotor invariants. The agents are controlled by continuous timed recurrent neural networks (CTRNNs) which bind their sensors and motors within a dynamic system. The behavioral ontogenesis is based on a phylogenetic approach: memorization occurs during the agent’s lifetime and an evolutionary algorithm discovers CTRNN parameters. This shows that sensorimotor invariants can be durably modified through interaction with a guiding agent. After this phase has finished, agents are able to adopt new sensorimotor invariants relative to the environment with no further guidance. We obtained these kinds of behaviors for CTRNNs with 3–6 units, and this paper examines the functioning of those CTRNNs. For instance, they are able to internally simulate guidance when it is externally absent, in line with theories of simulation in neuroscience and the enactive field of cognitive science.

Key words: ontogeny, evolutionary robotics, embodied agent, sensorimotor invariants, ctrnn, memory, enaction.