In the 1950s, the famous cyberneticists Gordon Pask and Stafford Beer conducted a series of remarkable electrochemical deposition experiments. By applying an electric potential across electrodes submerged in an acidic solution of ferrous sulfate, they could bias the growth of electrochemical deposition so as to form functional structures including sensory structures capable of distinguishing between different sounds. Unfortunately, the details of their apparatus and methods are unavailable. As a consequence, their experiment has not been replicated, and the precise mechanisms underlying their results remain unknown. As preliminary steps toward recreating their remarkable results, this paper presents a new computational model that simulates the growth and decay of dendritic structures similar to those investigated by Beer & Pask. We use this model to demonstrate a plausible mechanism through which an electrochemical system of this kind could respond to a reinforcement signal. More specifically, we investigate three strategies for varying the applied electrical current so as to guide the formation of structures into target forms. Each presented strategy succeeds at influencing the growth of the structure, with the most successful strategy involving a ‘constant-current’ feedback mechanism combined with an externally driven oscillation. In the discussion, we compare the adaptation of these structures with various biological adaptive processes, including evolution and metabolism-based adaptive behaviour.

A habit is formed through the repeated enactment of sensorimotor regularities created and maintained by means of plastic changes on the mechanism that brings them about. This precarious, self-maintaining sensorimotor organization is known as sensorimotor autonomy. One can imagine how some habits would be better suited to the maintenance of a biological individual. Evolution can bias the parameters of the plastic medium over which sensorimotor autonomy emerge so as to be beneficial to biological autonomy. In this work, we show that varying some parameters that bring about plastic changes in the behavior-generating medium, different sensorimotor individuals emerge. The simulation consists of a simple robot coupled with a habit-based controller with a random-based exploratory phase in a one-dimensional environment. The results show that, varying the parameters of such a phase, qualitative different habits emerge characterized by static, monotonic and oscillatory behaviors. Quantitative variations of the oscillatory behavior are also shown using the frequencies distribution obtained from the motor time series of the formed habits. The results are interpreted in terms of how the sensorimotor habitat could emerge from the random traversing of the sensorimotor environment. Finally, a comparison between this model and the skin brain thesis is presented.

Agency is an under-investigated foundational concept in understanding natural minds and how they differ from existing artificial forms of intelligence. To address this, Barandiaran et al. (2009) outlined a provisional definition of minimal agency, based upon three criteria: autonomous individuality; asymmetrical agent-environment interaction; and norm-driven modulation of that interaction. The first part of this paper reviews this definition, drawing attention to the interaction between interactional asymmetry and normativity. The definition is then applied to self-maintaining sensorimotor dynamics observed in a computational model. This has two broad goals: (i) improving our understanding of Barandiaran et al.’s definition of agency and how it could be applied to sensorimotor dynamics; and (ii) improving our understanding of the agent-like structures observed in a simulation of a simple robot whose sensors and motors are coupled to an iterant deformable sensorimotor medium (IDSM). I argue that specific structures within the simulation qualify as autonomous individuals and that these individuals can adapt to environmental changes in a way that benefits their viability. The nature of this adaptation is then examined by comparison to metabolism-independent and metabolism-based form of bacterial chemotaxis.

Lag is a well recognized phenomenon among both neuroscientists and engineers, with nervous tissues, sensors, actuators and other materials often demonstrating non-negligible lagged influences. A primary focus in evolutionary robotics is the dynamical nature of cognition, but the most common approaches –e. g. evolved continuous-time recurrent neural networks (CTRNN)– employ systems of ordinary differential equations that cannot directly capture lagged influence. Engineers, control-theorists, and neuroscientists often view lag as a problem that needs to be compensated for or avoided, but in this work, we present the first stages of our investigation into how lag can be functional , i. e. can underlie ‘intelligent’ behaviours. To do so, we follow the evolutionary robotics method, using a genetic algorithm to optimise the parameters of a controller that is coupled to a simulated robot, and then performing dynamical analysis on the most successful system identified. Unlike previous ER work, the controller is modelled using delay differential equations (DDE), so as to be able to capture lagged influence. The evolved controller is highly constrained, consisting of one modeled ‘neuron’ with a single lagged recurrent connection, so as to force the system to use lag as part of its solution. The artificial evolution identifies a high-quality solution, and we present our initial dynamical systems analysis of that individual.

A new method is presented for quantifying viability, the likelihood of a system persisting. We then present an information-theoretical approach for evaluating the quality of viability-indicators, measurable quantities that co-vary with, and thus can be used to predict or influence a system's viability.