A Top-Down Approach for a Synthetic Autobiographical Memory System

Autobiographical memory (AM) refers to the organisation of one’s experience into a coherent narrative. The exact neural mechanisms responsible for the manifestation of AM in humans are unknown. On the other hand, the field of psychology has provided us with useful understanding about the functionality of a bio-inspired synthetic AM (SAM) system, in a higher level of description. This paper is concerned with a top-down approach to SAM, where known components and organisation guide the architecture but the unknown details of each module are abstracted. By using Bayesian latent variable models we obtain a transparent SAM system with which we can interact in a structured way. This allows us to reveal the properties of specific sub-modules and map them to functionality observed in biological systems. The top-down approach can cope well with the high performance requirements of a bio-inspired cognitive system. This is demonstrated in experiments using faces data

The Emergence of Action Sequences from Spatial Attention: Insight from Rodent-Like Robots

Animal behaviour is rich, varied, and smoothly integrated. One plausible model of its generation is that behavioural sub-systems compete to command effectors. In small terrestrial mammals, many behaviours are underpinned by foveation, since important effectors (teeth, tongue) are co-located with foveal sensors (microvibrissae, lips, nose), suggesting a central role for foveal selection and foveation in generating behaviour. This, along with research on primate visual attention, inspires an alternative hypothesis, that integrated behaviour can be understood as sequences of foveations with selection being amongst foveation targets based on their salience. Here, we investigate control architectures for a biomimetic robot equipped with a rodent-like vibrissal tactile sensing system, explicitly comparing a salience map model for action guidance with an earlier model implementing behaviour selection. Both architectures generate life-like action sequences, but in the salience map version higher-level behaviours are an emergent consequence of following a shifting focus of attention

No proof for subjective experience in insects

Cruse H, Schilling M. No proof for subjective experience in insects. Animal Sentience. 2016;1(9): 123

The Vertical Optic Flow: An Additional Cue for Stabilizing Beerotor Robot’s Flight Without IMU

International audienceBio-inspired guidance principles involving no reference frame are presented here and were implemented in a rotorcraft called Beerotor, which was equipped with a minimalistic panoramic optic flow sensor and no accelerometer, no inertial measurement unit (IMU) [9], as in flying insects (Dipterian only uses rotation rates). In the present paper, the vertical optic flow was used as an additional cue whereas the previously published Beerotor II's visuo-motor system only used translational op-tic flow cues [9]. To test these guidance principles, we built a tethered tandem rotorcraft called Beerotor (80g), which flies along a high-roofed tunnel. The aerial robot adjusts its pitch and hence its speed, hugs the ground and lands safely without any need for an inertial reference frame. The rotorcraft's altitude and forward speed are adjusted via several op-tic flow feedback loops piloting respectively the lift and the pitch angle on the basis of the common-mode and differential rotor speeds, respectively as well as an active system of reorientation of a quasi-panoramic eye which constantly realigns its gaze, keeping it parallel to the nearest surface followed. Safe automatic terrain following and landing were obtained with the active eye-reorientation system over rugged terrain, without any need for an inertial reference frame

Enzyme Powered Nanomotors Towards Biomedical Applications

[eng] The advancements in nanotechnology enabled the development of new diagnostic tools and drug delivery systems based on nanosystems, which offer unique features such as large surface area to volume ratio, cargo loading capabilities, increased circulation times, as well as versatility and multifunctionality. Despite this, the majority of nanomedicines do not translate into clinics, in part due to the biological barriers present in the body. Synthetic nano- and micromotors could be an alternative tool in nanomedicine, as the continuous propulsion force and potential to modulate the medium may aid tissue penetration and drug diffusion across biological barriers. Enzyme-powered motors are especially interesting for biomedical applications, owing to their biocompatibility and use of bioavailable substrates as fuel for propulsion. This thesis aims at exploring the potential applications of urease-powered nanomotors in nanomedicine. In the first work, we evaluated these motors as drug delivery systems. We found that active urease- powered nanomotors showed active motion in phosphate buffer solutions, and enhanced in vitro drug release profiles in comparison to passive nanoparticles. In addition, we observed that the motors were more efficient in delivering drug to cancer cells and caused higher toxicity levels, due to the combination of boosted drug release and local increase of pH produced by urea breakdown into ammonia and carbon dioxide. One of the major goals in nanomedicine is to achieve localized drug action, thus reducing side-effects. A commonly strategy to attain this is the use moieties to target specific diseases. In our second work, we assessed the ability of urease-powered nanomotors to improve the targeting and penetration of spheroids, using an antibody with therapeutic potential. We showed that the combination of active propulsion with targeting led to a significant increase in spheroid penetration, and that this effect caused a decrease in cell proliferation due to the antibody’s therapeutic action. Considering that high concentrations of nanomedicines are required to achieve therapeutic efficiency; in the third work we investigated the collective behavior of urease-powered nanomotors. Apart from optical microscopy, we evaluated the tracked the swarming behavior of the nanomotors using positron emission tomography, which is a technique widely used in clinics, due to its noninvasiveness and ability to provide quantitative information. We showed that the nanomotors were able to overcome hurdles while swimming in confined geometries. We observed that the nanomotors swarming behavior led to enhanced fluid convection and mixing both in vitro, and in vivo within mice’s bladders. Aiming at conferring protecting abilities to the enzyme-powered nanomotors, in the fourth work, we investigated the use of liposomes as chassis for nanomotors, encapsulating urease within their inner compartment. We demonstrated that the lipidic bilayer provides the enzymatic engines with protection from harsh acidic environments, and that the motility of liposome-based motors can be activated with bile salts. Altogether, these results demonstrate the potential of enzyme-powered nanomotors as nanomedicine tools, with versatile chassis, as well as capability to enhance drug delivery and tumor penetration. Moreover, their collective dynamics in vivo, tracked using medical imaging techniques, represent a step-forward in the journey towards clinical translation.[spa] Recientes avances en nanotecnología han permitido el desarrollo de nuevas herramientas para el diagnóstico de enfermedades y el transporte dirigido de fármacos, ofreciendo propiedades únicas como encapsulación de fármacos, el control sobre la biodistribución de estos, versatilidad y multifuncionalidad. A pesar de estos avances, la mayoría de nanomedicinas no consiguen llegar a aplicaciones médicas reales, lo cual es en parte debido a la presencia de barreras biológicas en el organismo que limitan su transporte hacia los tejidos de interés. En este sentido, el desarrollo de nuevos micro- y nanomotores sintéticos, capaces de autopropulsarse y causar cambios locales en el ambiente, podrían ofrecer una alternativa para la nanomedicina, promoviendo una mayor penetración en tejidos de interés y un mejor transporte de fármacos a través de las barreras biológicas. En concreto, los nanomotores enzimáticos poseen un alto potencial para aplicaciones biomédicas gracias a su biocompatibilidad y a la posibilidad de usar sustancias presentes en el organismo como combustible. Los trabajos presentados en esta tesis exploran el potenical de nanomotores, autopropulsados mediante la enzima ureasa, para aplicaciones biomédicas, y investigan su uso como vehículos para transporte de fármacos, su capacidad para mejorar penetración de tejidos diana, su versatilidad y movimiento colectivo. En conjunto, los resultados presentados en esta tesis doctoral demuestran el potencial del uso de nanomotores autopropulsados mediante enzimas como herramientas biomédicas, ofreciendo versatilidad en su diseño y una alta capacidad para promover el transporte de fármacos y la penetración en tumores. Por último, su movimiento colectivo observado in vivo mediante técnicas de imagen médicas representan un significativo avance en el viaje hacia su aplicación en medicina

Développement d’une méthode d’apprentissage par projet pour l’enseignement de la modélisation multicorps appliquée au corps humain

RÉSUMÉ La modélisation multicorps est un outil d’ingénierie très utilisé à travers le monde pour résoudre des problèmes de cinématique et de dynamique de divers mécanismes. Son application au corps humain a vécu une grande révolution au cours des dernières décennies dans le milieu de la recherche, permettant notamment d’estimer les forces musculaires et les couples articulaires de manière non invasive. Le recours à des modèles humains est donc devenu de plus en plus populaire et pertinent pour l’industrie des produits de santé et les applications cliniques. En outre, la modélisation multicorps s’intègre de plus en plus dans les processus de décision pour la conception de produits tels que les exosquelettes, les prothèses, les orthèses ou encore l’évaluation fonctionnelle du corps humain. En particulier, beaucoup d’efforts ont été effectués dans les dernières années pour combiner cet outil avec d’autres outils tels que les logiciels de conception assistée par ordinateur et d’éléments finis afin de pouvoir faire des études plus complètes de conception et d’analyse. Or, malgré le fait que la modélisation multicorps est très complexe, cette matière est relativement peu enseignée de manière systématique, et généralement apprise sur le tas en recherche ou en industrie, limitant grandement les capacités d’utilisation et de développement des ingénieurs. Par conséquent, il est nécessaire de mettre en place une méthodologie d’apprentissage permettant d’intégrer cette matière dans la formation des ingénieurs en biomédical et en mécanique. Ainsi, le but de cette thèse de maitrise est de proposer une méthodologie d’apprentissage par projet pour faciliter l’enseignement des bases de la modélisation multicorps appliquée au corps humain, afin que les étudiants puissent ensuite envisager des développements plus avancés sur base d’un socle de compétences solide et standardisé. La méthode générale a consisté à identifier le matériel, les méthodologies et les défis des milieux professionnels de la modélisation multicorps. Ensuite, un projet pilote a été proposé à une classe de cycles supérieurs de génie biomédical, suivi d’une étape d’identification des difficultés et des défis de l’apprentissage de la modélisation multicorps dans la littérature et par le biais d’entrevues. Enfin, une méthodologie d’apprentissage par projet a été construite en se basant sur les méthodologies et matériels identifiés dans le milieu professionnel et répondant aux difficultés identifiées. Les résultats principaux de cette étude permettent (1) d’identifier les difficultés principales relatives à l’apprentissage et à l’utilisation de la modélisation multicorps appliquée au corps humain (2) de conclure que la méthodologie de projet ne doit pas seulement utiliser de la simulation mais doit s’accompagner d’un dispositif physique. En particulier, les résultats montrent que l’utilisation de prototypage rapide permet de proposer un projet simplifié tout en restant concret et en répondant aux difficultés identifiées.Les perspectives de cette étude sont de développer une méthodologie avancée augmentant la complexité du projet et du dispositif physique pour atteindre des modèles d’une sophistication semblable aux modèles utilisés dans l’industrie et la clinique.----------ABSTRACT Multibody modeling is an engineering tool widely used to solve kinematics and dynamics problems for various mechanisms. Its application to the human body modeling by the research community has gone through a revolution in the last decades, enabling to estimate muscle forces and joint torques in a non-invasive way. Therefore, the use of human-like models has become increasingly popular and very relevant to the health industry and for clinical applications. In addition, multibody modeling is more and more involved in the decision-making process for the design of products interacting closely with the human body such as exoskeletons or prosthetics. Particularly, many efforts have been made recently to combine this tool with other tools such as computer-aided design software packages and finite elements analysis in order to make more thorough design and analysis studies. However, despite the complexity inherent to learning of multibody modeling, it is rarely taught in a systematic way, and is usually learned in ad-hoc manner in both research and or industry, thus limiting greatly the capacity of cooperation and development for engineers. Therefore, it is necessary to develop a learning methodology allowing one to incorporate this material in the training of engineers and more particularly biomedical and mechanical engineers. Therefore, the aim of this Masters thesis is to provide a project based learning methodology to facilitate the teaching of the basics of multibody modeling applied to the human body, so that students could then consider more advanced developments on the basis of stronger and better standardized skills. The general approach proposed in this master thesis is to build on the methodology of real-world project development in the field of biomedical and mechanical engineering involving multibody modeling steps, to offer a project using professional tools and techniques. Then a step of identification of the difficulties and challenges for learning multibody modeling is carried out using data collected from literature and from semi-structured interviews leading to a proposed project-based learning methodology meeting the identified challenges. The main results of this master project allow (1) to identify the main difficulties in learning and using multibody modeling applied to the human body (2) to conclude that the proposed methodology should not only use simulation but must be accompanied by a physical prototype. In particular, the results show that the use of rapid prototyping enables one to offer a simplified project while still addressing the identified challenges. The prospects of this study are to develop a methodology increasing both the project and the physical device complexity to reach a similar sophistication compared with models used in the industry and by clinicians

The Ecology of Open-Ended Skill Acquisition: Computational framework and experiments on the interactions between environmental, adaptive, multi-agent and cultural dynamics

An intriguing feature of the human species is our ability to continuously invent new problems and to proactively acquiring new skills in order to solve them: what is called open-ended skill acquisition (OESA). Understanding the mechanisms underlying OESA is an important scientific challenge in both cognitive science (e.g. by studying infant cognitive development) and in artificial intelligence (aiming at computational architectures capable of open-ended learning). Both fields, however, mostly focus on cognitive and social mechanisms at the scale of an individual’s life. It is rarely acknowledged that OESA, an ability that is fundamentally related to the characteristics of human intelligence, has been necessarily shaped by ecological, evolutionary and cultural mechanisms interacting at multiple spatiotemporal scales. In this thesis, I present a research program aiming at understanding, modelingand simulating the dynamics of OESA in artificial systems, grounded in theories studying its eco-evolutionary bases in the human species. It relies on a conceptual framework expressing the complex interactions between environmental, adaptive, multi-agent and cultural dynamics. Three main research questions are developed and I present a selection of my contributions for each of them.- What are the ecological conditions favoring the evolution of skill acquisition?- How to bootstrap the formation of a cultural repertoire in populations of adaptive agents?- What is the role of cultural evolution in the open-ended dynamics of human skill acquisition?By developing these topics, we will reveal interesting relationships between theories in human evolution and recent approaches in artificial intelligence. This will lead to the proposition of a humanist perspective on AI: using it as a family of computational tools that can help us to explore and study the mechanisms driving open-ended skill acquisition in both artificial and biological systems, as a way to better understand the dynamics of our own species within its whole ecological context. This document presents an overview of my scientific trajectory since the start of my PhD thesis in 2007, the detail of my current research program, a selection of my contributions as well as perspectives for future work

UNDERWATER BONDING WITH POLYMER MIMICS OF MUSSEL ADHESIVE PROTEINS

When it comes to underwater adhesion, shellfish are the true experts. Mussels, barnacles, and oysters attach to rocks with apparent ease. Yet our man-made glues often fail miserably when trying to stick in wet environments. Results described herein focus on poly[(3,4-dihydroxystyrene)-co-styrene], a polymer mimic of mussel adhesive proteins. Underwater bonding was examined as a function of several parameters including polymer molecular weight and composition. In doing so, several surprising results emerged. Poly[(3,4-dihydroxystyrene)-co-styrene] may be the strongest underwater adhesive found to date. Bonding even exceeded that of the reference biological system, live mussels. Adhesion was also found to be stronger under salt water versus deionized water. Such unexpected findings may contradict earlier proposals in which charged amino acids were thought to be key for mussel adhesive function. Taken together, these discoveries are helping us to both understand biological adhesion as well as develop new materials with properties not accessed previously

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs