Evolutionary robotics and neuroscience

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Spatial encoding in primate hippocampus during free navigation.

The hippocampus comprises two neural signals-place cells and θ oscillations-that contribute to facets of spatial navigation. Although their complementary relationship has been well established in rodents, their respective contributions in the primate brain during free navigation remains unclear. Here, we recorded neural activity in the hippocampus of freely moving marmosets as they naturally explored a spatial environment to more explicitly investigate this issue. We report place cells in marmoset hippocampus during free navigation that exhibit remarkable parallels to analogous neurons in other mammalian species. Although θ oscillations were prevalent in the marmoset hippocampus, the patterns of activity were notably different than in other taxa. This local field potential oscillation occurred in short bouts (approximately .4 s)-rather than continuously-and was neither significantly modulated by locomotion nor consistently coupled to place-cell activity. These findings suggest that the relationship between place-cell activity and θ oscillations in primate hippocampus during free navigation differs substantially from rodents and paint an intriguing comparative picture regarding the neural basis of spatial navigation across mammals

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Information driven self-organization of complex robotic behaviors

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.Comment: 29 pages, 12 figure

Generation of Whole-Body Expressive Movement Based on Somatical Theories

An automatic choreography method to generate lifelike body movements is proposed. This method is based on somatics theories that are conventionally used to evaluate human’s psychological and developmental states by analyzing the body movement. The idea of this paper is to use the theories in the inverse way: to facilitate generation of artificial body movements that are plausible regarding evolutionary, developmental and emotional states of robots or other non-living movers. This paper reviews somatic theories and describes a strategy for implementations of automatic body movement generation. In addition, a psychological experiment is reported to verify expression ability on body movement rhythm. This method facilitates to choreographing body movement of humanoids, animal-shaped robots, and computer graphics characters in video games

The Long-Rage Directional Behavior of the Nematode C. Elegans

Like any mobile organism, C. elegans relies on sensory cues to find food. In the absence of such cues, animals might display defined search patterns or other stereotyped behavior. The motion of C. elegans has previously been characterized as a sinusoid whose direction can be modulated by gradual steering or by sharp turns, reversals and omega bends. However, such a fine-grained behavioral description does not by itself predict the longrange features of the animals’ pattern of movement. Using large (24 cm x 24 cm) Petri dishes, we characterized the movement pattern of C. elegans in the absence of stimuli. To collect trajectories over such a large surface, we devised an imaging setup employing an array of consumer flatbed scanners. We have confirmed quantitatively the results obtained with the scanner-array setup with a camera imaging setup, in a more stringently homogeneous environment. Wild-type worms display striking behavior in the absence of food. The majority (~60%) of the animals’ paths displays persistence in the direction of motion over length scales that are 50-100 times the body-length of C. elegans. The overall direction of movement differs from animal to animal, suggesting that the directed motion we observe might not be interpreted as a taxis to an external cue in the experimental environment. Interestingly, animals appear to exhibit directionality at large scales despite nondirectional motion at smaller scales. We quantified the extent of local directional persistence by computing the autocorrelation function of the velocities. Unexpectedly, correlations in the direction of motion decay over time scales that are much faster than the scales over which directional persistence appears to be maintained. We sought to establish quantitatively that the worm motion is, in fact, biased. To determine whether a null, random walk-like model of locomotion could account for directional behavior, we generated synthetic trajectories drawing from the same angle and step distributions of individual trajectories, and quantified the probabilities of obtaining larger net displacements than the experimental. Such a model fails to reproduce the experimental results. Moreover, the mean square displacements computed for the data display non-diffusive behavior, further demonstrating that the observed directional persistence cannot be explained by a simple random-walk model. To corroborate the hypothesis of biased movement in a model-independent fashion, we employed a geometrical characterization of the trajectories. Isotropic, unbiased walks result in paths that display a random distribution of turning angles between consecutive segments. In contrast, parsing of the worm’s trajectories yields different results depending on the segmentation scale adopted. In fact, increasing the segment size results in increasingly narrow turning angle distributions, centered around the zero. This suggests the emergence of directional coherence at long time scales. In order to investigate whether directional persistence is attained by a sensory mechanism, we analyzed the paths displayed by animals with impaired sensory function. Animals mutant for che-2, which display disrupted ciliary morphology and pleiotropic behavioral defects, exhibited non-directional behavior. Surprisingly however, daf-19 mutants, which lack sensory cilia altogether, displayed residual directionality, albeit at a lower penetrance (~20%) than the wild-type. This result suggests that directionality might implicate sensory modalities that do not require ciliary function, such as AFD-mediated thermosensation or URX-mediated oxygen sensation. Alternatively, the behavior of daf-19 mutants might imply that neural activity, but not sensory inputs, are required to achieve directed motion. Mutations in osm-9, a TRPV channel implicated in several avoidance behaviors in the worm, did not result in an observable phenotype. In contrast, mutations in tax-2/tax-4, a cGMP-gated channel required to transduce a number of sensory stimuli, resulted in loss of directionality. However, specific mutations targeting the signal transduction pathways for thermotaxis, olfaction, phototaxis, and aerotaxis, upstream of TAX-4, did not disrupt directional behavior. To get further insight into the nature of the stimulus directing the animals’ behavior, if any, we performed rescue experiments of TAX-4 function in specific subsets of neurons. In agreement with the results obtained by genetic lesions in the signal transduction pathways for thermotaxis and odortaxis, no rescue of directional behavior was observed when expressing TAX-4 in the thermosensory neuron AFD, or in the olfactory neurons AWB and AWC. Partial rescue of wild-type behavior was obtained by expression of TAX-4 in a set of five cells, which comprised the oxygen-sensing AQR, PQR and URX neurons as well as the ASJ and ASK sensory neurons, which transduce chemical stimuli and responses to dauer pheromone. To address the concern that the animals’ motion might be directed to a chemosensory cue within the plate, we investigated the correlation between path directions displayed by animals that were assayed on a same plate. We did not observe a detectable correlation between path headings, indicating that the worm is not chemotaxing to a plate-specific cue. In conclusion, our results indicate that the motion of C. elegans cannot be assimilated to a random walk, and that directional persistence arises at long times despite local nondirectional behavior. In addition, although we have not conclusively ruled out a sensorybased explanation, the genetic and phenomenological evidence gathered foreshadows the intriguing possibility that C. elegans might be achieving directional motion by relying solely on self-based information

Morphology of the Amorphous: Spatial texture, motion and words

On reflection upon my own experience as a composer in theory and practice, I find that terms not only define but also propose. In finding words for spatial texture, I have discovered that spatial texture itself is always proposing: motion of sound suggests texture of space. The texture of space creates an elusive materiality. Form is no better than formlessness

Social Integrating Robots Suggest Mitigation Strategies for Ecosystem Decay

We develop here a novel hypothesis that may generate a general research framework of how autonomous robots may act as a future contingency to counteract the ongoing ecological mass extinction process. We showcase several research projects that have undertaken first steps to generate the required prerequisites for such a technology-based conservation biology approach. Our main idea is to stabilise and support broken ecosystems by introducing artificial members, robots, that are able to blend into the ecosystem's regulatory feedback loops and can modulate natural organisms' local densities through participation in those feedback loops. These robots are able to inject information that can be gathered using technology and to help the system in processing available information with technology. In order to understand the key principles of how these robots are capable of modulating the behaviour of large populations of living organisms based on interacting with just a few individuals, we develop novel mathematical models that focus on important behavioural feedback loops. These loops produce relevant group-level effects, allowing for robotic modulation of collective decision making in social organisms. A general understanding of such systems through mathematical models is necessary for designing future organism-interacting robots in an informed and structured way, which maximises the desired output from a minimum of intervention. Such models also help to unveil the commonalities and specificities of the individual implementations and allow predicting the outcomes of microscopic behavioural mechanisms on the ultimate macroscopic-level effects. We found that very similar models of interaction can be successfully used in multiple very different organism groups and behaviour types (honeybee aggregation, fish shoaling, and plant growth). Here we also report experimental data from biohybrid systems of robots and living organisms. Our mathematical models serve as building blocks for a deep understanding of these biohybrid systems. Only if the effects of autonomous robots onto the environment can be sufficiently well predicted can such robotic systems leave the safe space of the lab and can be applied in the wild to be able to unfold their ecosystem-stabilising potential