(A)Life as It Could Be.

On this 30th anniversary of the founding of the Artificial Life journal, I share some personal reflections on my own history of engagement with the field, my own particular assessment of its current status, and my vision for its future development. At the very least, I hope to stimulate some necessary critical conversations about the field of Artificial Life and where it is going.

Milking a spherical cow: Toy models in neuroscience.

There are many different kinds of models, and they play many different roles in the scientific endeavour. Neuroscience, and biology more generally, has understandably tended to emphasise empirical models that are grounded in data and make specific, experimentally testable predictions. Meanwhile, strongly idealised or 'toy' models have played a central role in the theoretical development of other sciences such as physics.

On the Proper Treatment of Dynamics in Cognitive Science.

This essay examines the relevance of dynamical ideas for cognitive science. On its own, the mere mathematical idea of a dynamical system is too weak to serve as a scientific theory of anything, and dynamical approaches within cognitive science are too rich and varied to be subsumed under a single "dynamical hypothesis." Instead, after first attempting to dissect the different notions of "dynamics" and "cognition" at play, a more specific theoretical framework for cognitive science broadly construed is sketched.

The theoretical foundations of enaction: Precariousness.

Enaction is an increasingly influential approach to cognition that grew out of Maturana and Varela's earlier work on autopoiesis and the biology of cognition. As with any relatively new scientific discipline, the enactive approach would benefit greatly from a careful analysis of its theoretical foundations. Here we initiate such an analysis for one of the core concepts of enaction, precariousness.

Codimension-2 parameter space structure of continuous-time recurrent neural networks.

If we are ever to move beyond the study of isolated special cases in theoretical neuroscience, we need to develop more general theories of neural circuits over a given neural model. The present paper considers this challenge in the context of continuous-time recurrent neural networks (CTRNNs), a simple but dynamically universal model that has been widely utilized in both computational neuroscience and neural networks. Here, we extend previous work on the parameter space structure of codimension-1 local bifurcations in CTRNNs to include codimension-2 local bifurcation manifolds.

A Neuromechanical Model of Multiple Network Rhythmic Pattern Generators for Forward Locomotion in .

Multiple mechanisms contribute to the generation, propagation, and coordination of the rhythmic patterns necessary for locomotion in . Current experiments have focused on two possibilities: pacemaker neurons and stretch-receptor feedback. Here, we focus on whether it is possible that a chain of multiple network rhythmic pattern generators in the ventral nerve cord also contribute to locomotion.

Control of visually guided braking using constant- and proportional rate.

This study investigated the optical information and control strategies used in visually guided braking. In such tasks, drivers exhibit two different braking behaviors: impulsive braking and continuously regulated braking. We designed two experiments involving a simulated braking task to investigate these two behaviors.

An Investigation into the Origin of Autopoiesis.

Using a glider in the Game of Life cellular automaton as a toy model of minimal persistent individuals, this article explores how questions regarding the origin of life might be approached from the perspective of autopoiesis. Specifically, I examine how the density of gliders evolves over time from random initial conditions and then develop a statistical mechanics of gliders that explains this time evolution in terms of the processes of glider creation, persistence, and destruction that underlie it.

Potential role of a ventral nerve cord central pattern generator in forward and backward locomotion in .

locomotes in an undulatory fashion, generating thrust by propagating dorsoventral bends along its body. Although central pattern generators (CPGs) are typically involved in animal locomotion, their presence in has been questioned, mainly because there has been no evident circuit that supports intrinsic network oscillations. With a fully reconstructed connectome, the question of whether it is possible to have a CPG in the ventral nerve cord (VNC) of can be answered through computational models.

From head to tail: a neuromechanical model of forward locomotion in .

With 302 neurons and a near-complete reconstruction of the neural and muscle anatomy at the cellular level, is an ideal candidate organism to study the neuromechanical basis of behaviour. Yet despite the breadth of knowledge about the neurobiology, anatomy and physics of , there are still a number of unanswered questions about one of its most basic and fundamental behaviours: forward locomotion. How the rhythmic pattern is generated and propagated along the body is not yet well understood.

Computing aggregate properties of preimages for 2D cellular automata.

Computing properties of the set of precursors of a given configuration is a common problem underlying many important questions about cellular automata. Unfortunately, such computations quickly become intractable in dimension greater than one. This paper presents an algorithm-incremental aggregation-that can compute aggregate properties of the set of precursors exponentially faster than naïve approaches.

The Structure of Ontogenies in a Model Protocell.

Emergent individuals are often characterized with respect to their viability: their ability to maintain themselves and persist in variable environments. As such individuals interact with an environment, they undergo sequences of structural changes that correspond to their ontogenies. Ultimately, individuals that adapt to their environment, and increase their chances of survival, persist.

The whole worm: brain-body-environment models of C. elegans.

Brain, body and environment are in continuous dynamical interaction, and it is becoming increasingly clear that an animal's behavior must be understood as a product not only of its nervous system, but also of the ongoing feedback of this neural activity through the biomechanics of its body and the ecology of its environment. Modeling has an essential integrative role to play in such an understanding. But successful whole-animal modeling requires an animal for which detailed behavioral, biomechanical and neural information is available and a modeling methodology which can gracefully cope with the constantly changing balance of known and unknown biological constraints.

Exploring the Space of Viable Configurations in a Model of Metabolism-Boundary Co-construction.

We introduce a spatial model of concentration dynamics that supports the emergence of spatiotemporal inhomogeneities that engage in metabolism-boundary co-construction. These configurations exhibit disintegration following some perturbations, and self-repair in response to others. We define robustness as a viable configuration's tendency to return to its prior configuration in response to perturbations, and plasticity as a viable configuration's tendency to change to other viable configurations.

Information Flow through a Model of the C. elegans Klinotaxis Circuit.

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans.

Characterizing autopoiesis in the game of life.

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 article investigates its application to the compact, recurrent spatiotemporal patterns that arise in Conway's Game-of-Life cellular automaton. In particular, we demonstrate how such entities can be formulated as self-constructing networks of interdependent processes that maintain their own boundaries.

Information processing and dynamics in minimally cognitive agents.

There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory.

The cognitive domain of a glider in the game of life.

This article examines in some technical detail the application of Maturana and Varela's biology of cognition to a simple concrete model: a glider in the game of Life cellular automaton. By adopting an autopoietic perspective on a glider, the set of possible perturbations to it can be divided into destructive and nondestructive subsets. From a glider's reaction to each nondestructive perturbation, its cognitive domain is then mapped.

Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis.

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior.

Partial information decomposition as a spatiotemporal filter.

Understanding the mechanisms of distributed computation in cellular automata requires techniques for characterizing the emergent structures that underlie information processing in such systems. Recently, techniques from information theory have been brought to bear on this problem. Building on this work, we utilize the new technique of partial information decomposition to show that previous information-theoretic measures can confound distinct sources of information.

Beyond control: the dynamics of brain-body-environment interaction in motor systems.

Discussions of motor behavior have traditionally focused on how a nervous system controls a body. However, it has become increasingly clear that a broader perspective, in which motor behavior is seen as arising from the interaction between neural and biomechanical dynamics, is needed. This chapter reviews a line of work aimed at exploring this perspective in a simple model of walking.

Developmental bias in evolution: evolutionary accessibility of phenotypes in a model evo-devo system.

The success of the modern synthesis has resulted in forces of evolutionary change other than natural selection being marginalized. However, recent work has attempted to show the importance of non-selective influences in shaping organic form. One such force is developmental bias, in which phenotypes are differentially produced.

Parameter space structure of continuous-time recurrent neural networks.

A fundamental challenge for any general theory of neural circuits is how to characterize the structure of the space of all possible circuits over a given model neuron. As a first step in this direction, this letter begins a systematic study of the global parameter space structure of continuous-time recurrent neural networks (CTRNNs), a class of neural models that is simple but dynamically universal. First, we explicitly compute the local bifurcation manifolds of CTRNNs.

Design and implementation of multipattern generators in analog VLSI.

In recent years, computational biologists have shown through simulation that small neural networks with fixed connectivity are capable of producing multiple output rhythms in response to transient inputs. It is believed that such networks may play a key role in certain biological behaviors such as dynamic gait control. In this paper, we present a novel method for designing continuous-time recurrent neural networks (CTRNNs) that contain multiple embedded limit cycles, and we show that it is possible to switch the networks between these embedded limit cycles with simple transient inputs.