Evolutionary robotics and neuroscience

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Schizophrenia and the Dysfunctional Brain

Scientists, philosophers, and even the lay public commonly accept that schizophrenia stems from a biological or internal ‘dysfunction.’ However, this assessment is typically accompanied neither by well-defined criteria for determining that something is dysfunctional nor empirical evidence that schizophrenia satisfies those criteria. In the following, a concept of biological function is developed and applied to a neurobiological model of schizophrenia. It concludes that current evidence does not warrant the claim that schizophrenia stems from a biological dysfunction, and, in fact, that unusual neural structures associated with schizophrenia may have functional or adaptive significance. The fact that current evidence is ambivalent between these two possibilities (dysfunction versus adaptive function) implies that schizophrenia researchers should be much more cautious in using the ‘dysfunction’ label than they currently are. This has implications for both psychiatric treatment as well as public perception of mental disorders

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

Low-mass star formation in CG1: a diffraction limited search for pre-main sequence stars next to NX Puppis

Using adaptive optics at the ESO 3.6m telescope, we obtained diffraction limited JHK-images of the region around the Herbig AeBe star NX Pup. We clearly resolved the close companion (sep. 0.128") to NX Pup -- originally discovered by HST -- and measured its JHK magnitudes. A third object at a separation of 7.0" from NX Pup was identified as a classical T Tauri star so that NX Pup may in fact form a hierarchical triple system. We discuss the evolutionary status of these stars and derive estimates for their spectral types, luminosities, masses and ages.Comment: Latex using l-aa-ps.sty with links to 5 postscript figures. Complete postscript version also available at http://lucky.astro.uni-wuerzburg.de/ Accepted for publication in A&

Evolving collective behavior in an artificial ecology

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems

Neuroethology, Computational

Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents

Maladaptation and the paradox of robustness in evolution

Background. Organisms use a variety of mechanisms to protect themselves against perturbations. For example, repair mechanisms fix damage, feedback loops keep homeostatic systems at their setpoints, and biochemical filters distinguish signal from noise. Such buffering mechanisms are often discussed in terms of robustness, which may be measured by reduced sensitivity of performance to perturbations. Methodology/Principal Findings. I use a mathematical model to analyze the evolutionary dynamics of robustness in order to understand aspects of organismal design by natural selection. I focus on two characters: one character performs an adaptive task; the other character buffers the performance of the first character against perturbations. Increased perturbations favor enhanced buffering and robustness, which in turn decreases sensitivity and reduces the intensity of natural selection on the adaptive character. Reduced selective pressure on the adaptive character often leads to a less costly, lower performance trait. Conclusions/Significance. The paradox of robustness arises from evolutionary dynamics: enhanced robustness causes an evolutionary reduction in the adaptive performance of the target character, leading to a degree of maladaptation compared to what could be achieved by natural selection in the absence of robustness mechanisms. Over evolutionary time, buffering traits may become layered on top of each other, while the underlying adaptive traits become replaced by cheaper, lower performance components. The paradox of robustness has widespread implications for understanding organismal design