Under what conditions will an organism remain viable as numerous forces threaten its self-construction, and what does this abstract space of possibilities look like? A growing body of work has begun to confront this question by imposing viability limits on dynamical system models to separate sets of viable and nonviable states. Since the viability limits are not implicit in the equations that govern the dynamics, there is no guaranteed equivalence between the phase portrait and the basins of initial conditions that will remain viable. This means that the topology of a dynamical system model with imposed viability limits demands richer analyses, which we refer to as characterizing viability space . In this paper, we set the groundwork for such techniques using a protocell model governed by nonlinear ordinary differential equations, including the development of novel criteria for bifurcations so that entire classes of systems can be studied.

Various models have been developed to shed light on neuronal mechanisms of homeostatic plasticity (HP). We focus on one such model implemented on continuous-time-recurrent neural networks. Though this HP mechanism encourages oscillatory dynamics by preventing node saturation, it was curiously detrimental to behavioral fitness when compared to non-plastic networks on several tasks (Williams, 2004, 2005). When we set out to explain this result, we discovered a type of oscillation that depends on HP’s continued regulation of circuit parameters. If HP is turned off, oscillation stops. This suggests that HP can play an enabling role in central pattern generation which has not been explored in modelling or experimental contexts. We first situate this phenomenon within the space of possibilities for HP’s involvement in oscillation. Then, we show that these “HP-enabled” oscillations are extraordinarily common in random circuits of various sizes. Finally, we describe how the degree of timescale separation between HP and neural dynamics affects HP-enabled oscillation. This analysis suggests promising avenues for dialogue between modeling and experiment.

Enaction's claim of continuity between life and mind is a bold one. We investigate one aspect of this claim using a glider in the Game of Life as a toy model. Specifically, we study the relationship between theories of glider constitution and glider interaction, demonstrating how a glider's constitution completely determines its interaction graph, but not the particular life that it enacts, which also requires knowledge of the dynamics of its environment.

Using a glider in the Game of Life cellular automaton as a toy model, we explore how questions of origins might be approached from the perspective of autopoiesis. Specifically, we 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.

This paper presents results from a series of evolutionary robotics simulations that were designed to investigate the informational basis of visually-guided braking. Evolutionary robotics techniques were used to develop models of visually-guided braking behavior in humans to aid in resolving existing questions in the literature. Based on a well-used experimental paradigm from psychology, model agents were evolved to solve a driving-like braking task in a simple 2D environment involving one object. Agents had five sensors to detect image size of the object, image expansion rate, tau, tau-dot and proportional rate, respectively. These optical variables were those tested in experimental investigations of visually-guided braking in humans. The aim of the present work was to investigate which of these optical variables were used by the evolved agents to solve the braking task when all variables were available to control braking. Our results indicated that the agent with the highest performance used exclusively proportional rate to control braking. The agent with the lowest performance was found to be using primarily tau-dot together with image size and image expansion rate.

Organisms are embedded in environments, with which they engage in an ongoing two-way interaction called structural coupling. It is in this context that an organism develops, behaves, thrives, and ultimately dies. This paper introduces a network-based methodology for analyzing how an organism and environment unfold together through structural coupling, and demonstrates this methodology in a cellular Potts model. A morphology-environment transition network consists of all reachable combinations of morphological and environmental states as its nodes, and the transitions between these morphology/ environment states as its edges. In a given simulation, the model cell and its environment move through this network as both dynamically unfold. Analysis of such a network reveals several interesting properties, including attractor states, divergence of network structure when the cell is placed in different environments, and niche construction in which the cell’s influence over its environment increases its own viability.

With 302 neurons and a fully reconstructed connectome, Caernohabditis elegans is an ideal candidate organism to study how behavior is grounded in the interaction between an organism's brain, its body, and its environment. Since nearly its entire behavioral repertoire is expressed through movement, understanding the neuromechanical basis of locomotion is especially critical as a foundation upon which analyses of all other behaviors must build. In this extended abstract, we report on the evolution and analysis of an integrated neuromechanical model of forward locomotion.