A Continuous Time Dynamical Turing Machine.

Continuous time recurrent neural networks (CTRNNs) are systems of coupled ordinary differential equations (ODEs) inspired by the structure of neural networks in the brain. CTRNNs are known to be universal dynamical approximators: given a large enough system, the parameters of a CTRNN can be tuned to produce output that is arbitrarily close to that of any other dynamical system. However, in practice, both designing systems of CTRNN to have a certain output, and the reverse-understanding the dynamics of a given system of CTRNN-can be nontrivial.

Using enactive robotics to think outside of the problem-solving box: How sensorimotor contingencies constrain the forms of emergent autononomous habits.

We suggest that the influence of biology in 'biologically inspired robotics' can be embraced at a deeper level than is typical, if we adopt an enactive approach that moves the focus of interest from how problems are solved to how problems emerge in the first place. In addition to being inspired by mechanisms found in natural systems or by evolutionary design principles directed at solving problems posited by the environment, we can take inspiration from the precarious, self-maintaining organization of living systems to investigate forms of cognition that are also precarious and self-maintaining and that thus also, like life, have their own problems that must be be addressed if they are to persist. In this vein, we use a simulation to explore precarious, self-reinforcing sensorimotor habits as a building block for a robot's behavior.

Goal Oriented Behavior With a Habit-Based Adaptive Sensorimotor Map Network.

We present a description of an ASM-network, a new habit-based robot controller model consisting of a network of adaptive sensorimotor maps. This model draws upon recent theoretical developments in enactive cognition concerning habit and agency at the sensorimotor level. It aims to provide a platform for experimental investigation into the relationship between networked organizations of habits and cognitive behavior.

Self-preserving mechanisms in motile oil droplets: a computational model of abiological self-preservation.

Recent empirical work has characterized -small, self-propelled oil droplets whose active surface chemistry moves them through their aqueous environment. Previous work has evaluated in detail the fluid dynamics underlying the motility of these droplets. This paper introduces a new computational model that is used to evaluate the behaviour of these droplets , whereby (i) the mechanism of motility causes motion towards the conditions beneficial to that mechanism's persistence; and (ii) the behaviour automatically adapts to compensate when the motility mechanism's ideal operating conditions change.

Can Signal Delay be Functional? Including Delay in Evolved Robot Controllers.

Engineers, control theorists, and neuroscientists often view the delay imposed by finite signal propagation velocities as a problem that needs to be compensated for or avoided. In this article, we consider the alternative possibility that in some cases, signal delay can be used , that is, as an essential component of a cognitive system. To investigate this idea, we evolve a minimal robot controller to solve a basic stimulus-distinction task.

From chemical soup to computing circuit: transforming a contiguous chemical medium into a logic gate network by modulating its external conditions.

It has been shown that it is possible to transform a well-stirred chemical medium into a logic gate simply by varying the chemistry's external conditions (feed rates, lighting conditions, etc.). We extend this work, showing that the same method can be generalized to spatially extended systems.

Combining evolution and self-organization to find natural Boolean representations in unconventional computational media.

Designing novel unconventional computing systems often requires the selection of the computational structure as well as choosing the right symbol encoding. Several approaches apply heuristic search and evolutionary algorithms to find both computational structure and symbol encoding, which is time consuming because they depend on each other. Here, we present a novel approach that combines evolution with self-organization, in particular we evolve the computational structure but let the symbol encoding emerge through self-organization.

Where Computation and Dynamics Meet: Heteroclinic Network-Based Controllers in Evolutionary Robotics.

In the fields of artificial neural networks and robotics, complicated, often high-dimensional systems can be designed using evolutionary/other algorithms to successfully solve very complex tasks. However, dynamical analysis of the underlying controller can often be near impossible, due to the high dimension and nonlinearities in the system. In this paper, we propose a more restricted form of controller, such that the underlying dynamical systems are forced to contain a dynamical object called a heteroclinic network.

Nanotube-mediated cross-feeding couples the metabolism of interacting bacterial cells.

Bacteria frequently engage in cross-feeding interactions that involve an exchange of metabolites with other micro- or macroorganisms. The often obligate nature of these associations, however, hampers manipulative experiments, thus limiting our mechanistic understanding of the ecophysiological consequences that result for the organisms involved. Here we address this issue by taking advantage of a well-characterized experimental model system, in which auxotrophic genotypes of E.

Dynamic modulation of external conditions can transform chemistry into logic gates.

We introduce a new method for transforming chemical systems into desired logical operators (e.g. NAND gates) or similar signal-manipulation components.

Methods for Measuring Viability and Evaluating Viability Indicators.

Life and other dissipative structures involve nonlinear dynamics that are not amenable to conventional analysis. Advances are being made in theory, modeling, and simulation techniques, but we do not have general principles for designing, controlling, stabilizing, or eliminating these systems. There is thus a need for tools that can transform high-level descriptions of these systems into useful guidance for their modification and design.

The Biological Foundations of Enactivism: A Report on a Workshop Held at Artificial Life XV.

This is a report on the Biological Foundations of Enactivism Workshop, which was held as part of Artificial Life XV. The workshop aimed to revisit enactivism's contributions to biology and to revitalize the discussion of autonomy with the goal of grounding it in quantitative definitions based in observable phenomena. This report summarizes some of the important issues addressed in the workshop's talks and discussions, which include how to identify emergent individuals out of an environmental background, what the roles of autonomy and normativity are in biological theory, how new autonomous agents can spontaneously emerge at the origins of life, and what science can say about subjective experience.

Adapting to Adaptations: Behavioural Strategies that are Robust to Mutations and Other Organisational-Transformations.

Genetic mutations, infection by parasites or symbionts, and other events can transform the way that an organism's internal state changes in response to a given environment. We use a minimalistic computational model to support an argument that by behaving "interoceptively," i.e.

Corrigendum: Modeling habits as self-sustaining patterns of sensorimotor behavior.

[This corrects the article on p. 590 in vol. 8, PMID: 25152724.

Modeling habits as self-sustaining patterns of sensorimotor behavior.

In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior.

Bacterial chemotaxis: introverted or extroverted? A comparison of the advantages and disadvantages of basic forms of metabolism-based and metabolism-independent behavior using a computational model.

Using a minimal model of metabolism, we examine the limitations of behavior that is (a) solely in response to environmental phenomena or (b) solely in response to metabolic dynamics, showing that basic forms of each of these kinds of behavior are incapable of driving survival-prolonging behavior in certain situations. Inspired by experimental evidence of concurrent metabolism-based and metabolism-independent chemotactic mechanisms in Escherichia coli and Rhodobacter sphaeroides, we then investigate how metabolism-independent and metabolism-based sensitivities can be integrated into a single behavioral response, demonstrating that a simple switching mechanism can be sufficient to effectively integrate metabolism-based and metabolism-independent behaviors. Finally, we use a spatial simulation of bacteria to show that the investigated forms of behavior produce different spatio-temporal patterns that are influenced by the metabolic-history of the bacteria.

Norm-establishing and norm-following in autonomous agency.

Living agency is subject to a normative dimension (good-bad, adaptive-maladaptive) that is absent from other types of interaction. We review current and historical attempts to naturalize normativity from an organism-centered perspective, identifying two central problems and their solution: (1) How to define the topology of the viability space so as to include a sense of gradation that permits reversible failure, and (2) how to relate both the processes that establish norms and those that result in norm-following behavior. We present a minimal metabolic system that is coupled to a gradient-climbing chemotactic mechanism.

Spatial rule-based modeling: a method and its application to the human mitotic kinetochore.

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed.

Behavioral metabolution: the adaptive and evolutionary potential of metabolism-based chemotaxis.

We use a minimal model of metabolism-based chemotaxis to show how a coupling between metabolism and behavior can affect evolutionary dynamics in a process we refer to as behavioral metabolution. This mutual influence can function as an in-the-moment, intrinsic evaluation of the adaptive value of a novel situation, such as an encounter with a compound that activates new metabolic pathways. Our model demonstrates how changes to metabolic pathways can lead to improvement of behavioral strategies, and conversely, how behavior can contribute to the exploration and fixation of new metabolic pathways.

A minimal model of metabolism-based chemotaxis.

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria.