Ulysses Bernardet

An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search

Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robots.

IQR: a distributed system for real-time real-world neuronal simulation

Abstract IQR is a new simulator which allows neuronal models to control the behaviour of real-world devices in real-time. Data from several levels of description can be combined. IQR uses a distributed architecture to provide real-time processing. We present the key features of IQR and highlight successful projects which have used this simulator.

iqr: A Tool for the Construction of Multi-level Simulations of Brain and Behaviour

The brain is the most complex system we know of. Despite the wealth of data available in neuroscience, our understanding of this system is still very limited. Here we argue that an essential component in our arsenal of methods to advance our understanding of the brain is the construction of artificial brain-like systems. In this way we can encompass the multi-level organisation of the brain and its role in the context of the complete embodied real-world and real-time perceiving and behaving system. Hence, on the one hand, we must be able to develop and validate theories of brains as closing the loop between perception and action, and on the other hand as interacting with the real world. Evidence is growing that one of the sources of the computational power of neuronal systems lies in the massive and specific connectivity, rather than the complexity of single elements. To meet these challenges—multiple levels of organisation, sophisticated connectivity, and the interaction of neuronal models with the real-world—we have developed a multi-level neuronal simulation environment, iqr. This framework deals with these requirements by directly transforming them into the core elements of the simulation environment itself. iqr provides a means to design complex neuronal models graphically, and to visualise and analyse their properties on-line. In iqr connectivity is defined in a flexible, yet compact way, and simulations run at a high speed, which allows the control of real-world devices—robots in the broader sense—in real-time. The architecture of iqr is modular, providing the possibility to write new neuron, and synapse types, and custom interfaces to other hardware systems. The code of iqr is publicly accessible under the GNU General Public License (GPL). iqr has been in use since 1996 and has been the core tool for a large number of studies ranging from detailed models of neuronal systems like the cerebral cortex, and the cerebellum, to robot based models of perception, cognition and action to large-scale real-world systems. In addition, iqr has been widely used over many years to introduce students to neuronal simulation and neuromorphic control. In this paper we outline the conceptual and methodological background of iqr and its design philosophy. Thereafter we present iqr’s main features and computational properties. Finally, we describe a number of projects using iqr, singling out how iqr is used for building a “synthetic insect”.

A Biologically Based Chemo-Sensing UAV for Humanitarian Demining:

Antipersonnel mines, weapons of cheap manufacture but lethal effect, have a high impact on the population even decades after the conflicts have finished. Here we investigate the use of a chemo-sensing Unmanned Aerial Vehicle (cUAV) for demining tasks. We developed a blimp based UAV that is equipped with a broadly tuned metal-thin oxide chemo-sensor. A number of chemical mapping strategies were investigated including two biologically based localization strategies derived from the moth chemical search that can optimize the efficiency of the detection and localization of explosives and therefore be used in the demining process. Additionally, we developed a control layer that allows for both fully autonomous and manual controlled flight, as well as for the scheduling of a fleet of cUAVs. Our results confirm the feasibility of this technology for demining in real-world scenarios and give further support to a biologically based approach where the understanding of biological systems is used to solve difficult engineering problems.

ALLOSTATIC CONTROL FOR ROBOT BEHAVIOR REGULATION: A COMPARATIVE RODENT-ROBOT STUDY

Rodents are optimal real-world foragers that regulate internal states maintaining a dynamic stability with their surroundings. How these internal drive based behaviors are regulated remains unclear. Based on the physiological notion of allostasis, we investigate a minimal control system able to approximate their behavior. Allostasis is the process of achieving stability with the environment through change, opposed to homeostasis which achieves it through constancy. Following this principle, the so-called allostatic control system orchestrates the interaction of the homeostatic modules by changing their desired values in order to achieve stability. We use a minimal number of subsystems and estimate the model parameters from rat behavioral data in three experimental setups: free exploration, presence of reward, delivery of cues with reward predictive value. From this analysis, we show that a rat is influenced by the shape of the arena in terms of its openness. We then use the estimated model configurations to control a simulated and real robot which captures essential properties of the observed rat behavior. The allostatic reactive control model is proposed as an augmentation of the Distributed Adaptive Control architecture and provides a further contribution towards the realization of an artificial rodent.

The eXperience Induction Machine: A New Paradigm for Mixed-Reality Interaction Design and Psychological Experimentation

The eXperience Induction Machine (XIM) is one of the most advanced mixed-reality spaces available today. XIM is an immersive space that consists of physical sensors and effectors and which is conceptualized as a general-purpose infrastructure for research in the field of psychology and human–artifact interaction. In this chapter, we set out the epistemological rational behind XIM by putting the installation in the context of psychological research. The design and implementation of XIM are based on principles and technologies of neuromorphic control. We give a detailed description of the hardware infrastructure and software architecture, including the logic of the overall behavioral control. To illustrate the approach toward psychological experimentation, we discuss a number of practical applications of XIM. These include the so-called, persistent virtual community, the application in the research of the relationship between human experience and multi-modal stimulation, and an investigation of a mixed-reality social interaction paradigm.

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the systems collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.

Allostatic control for robot behaviour regulation: An extension to path planning

Rodents are optimal real-world foragers that can smoothly regulate behaviors like homing and exploration combined with more elaborate abilities as food source localization. Here we investigate a robot based model that implements the self-regulatory processes that underly optimal foraging of rodents in unknown environments and is also able to combine it with goal directed behaviors. Behavior is decomposed into minimal homeostatic subsystems that regulate themselves through the local perception/detection of a gradient. On a higher level, the allostatic control orchestrates the interaction of the different homeostatic modules allowing it to dynamically manage the interactions between the desired values of each subsystem to achieve stability on a meta behavioral level. In this case, we show that overall behavioral stability can be achieved. We validate our model by comparing the behavior of both simulated and real robots with that of rodents. Our next step is then to justify gradients as a valid biological assumption by giving a biologically plausible process for generating them from a cognitive map, in this case, a set of approximated hippocampal place cells. We finally formulate path planning (used for goal reaching, e.g. food source localization) in the same context of a gradient map generation that can be then inserted as an additional subsystem of the higher meta level allostatic control.

The Social Perceptual Salience Effect

Affective processes appraise the salience of external stimuli preparing the agent for action. So far, the relationship between stimuli, affect, and action has been mainly studied in highly controlled laboratory conditions. In order to find the generalization of this relationship to social interaction, we assess the influence of the salience of social stimuli on human interaction. We constructed reality ball game in a mixed reality space where pairs of people collaborated in order to compete with an opposing team. We coupled the players with team members with varying social salience by using both physical and virtual representations of remote players (i.e., avatars). We observe that, irrespective of the team composition, winners and losers display significantly different inter- and intrateam spatial behaviors. We show that subjects regulate their interpersonal distance to both virtual and physical team members in similar ways, but in proportion to the vividness of the stimulus. As an independent validation of this social salience effect, we show that this behavioral effect is also displayed in physiological correlates of arousal. In addition, we found a strong correlation between performance, physiology, and the subjective reports of the subjects. Our results show that proxemics is consistent with affective responses, confirming the existence of a social salience effect. This provides further support for the so-called law of apparent reality, and it generalizes it to the social realm, where it can be used to design more efficient social artifacts.

Embodied interaction with complex neuronal data in mixed-reality

The study of natural and artificial phenomena generates massive amounts of data in many areas of research. This data is frequently left unused due to the lack of tools to effectively extract, analyze and understand it. Visual representation techniques can play a key role in helping to discover patterns and meaning within this data. Neuroscience is one of the scientific fields that generates the most extensive datasets. For this reason we built a 3D real-time visualization system to graphically represent the massive connectivity of neuronal network models in the eXperience Induction Machine (XIM). The XIM is an immersive space equipped with a number of sensors and effectors that we constructed to conduct experiments in mixed-reality. Using this infrastructure we developed an embodied interaction framework that allows the user to move freely in the space and navigate through the neuronal system. We conducted an empirical evaluation of the impact of different navigation mappings on the understanding of a neuronal dataset. Our results revealed that different navigation mappings affect the structural understanding of the system and the involvement with the data presented.