Active Neural Field model of goal directed eye-movements

International audienceFor primates (including humans), interacting with objects of interest in the environment often involves their foveation, many of them not being static (e.g. other animals, relative motion due to self-induced movement). Eye movements allow the active and continuous sampling of local information, exploiting the graded precision of visual signals (e.g., due to the types and distributions of photoreceptors). Foveating and tracking targets thus requires adapting to their motion. Indeed, considering the delays involved in the transmission of retinal signals to the eye muscles, a purely reactive schema could not account for the smooth pursuit movements which maintain the target within the central visual field. Internal models have been posited to represent the future position of the target (for instance extrapolating from past observations), in order to compensate for these delays. Yet, adaptation of the sensorimotor and neural activity may be sufficient to synchronize with the movement of the target, converging to encoding its location here-and-now, without explicitly resorting to any frame of reference (Goffart et al., 2017).Committing to a distributed dynamical systems approach, we relied on a computational implementation of neural fields to model an adaptation mechanism sufficient to select, focus and track rapidly moving targets. By coupling the generation of eye-movements with dynamic neural field models and a simple learning rule, we replicated neurophysiological results that demonstrated how the monkey adapts to repeatedly observed moving targets (Bourrelly et al., 2016; Quinton & Goffart, 2018), progressively reducing the number of catch-up saccades and increasing smooth pursuit velocity (yet not going beyond the here-and-now target location). We now focus on eye-movements observed in presence of two simultaneously moving centrifugal targets (Goffart, 2016), for which the reduction to a single trajectory with some predicted dynamics (e.g., target center) is even more inappropriate

Budgeting Under-Specified Tasks for Weakly-Hard Real-Time Systems

In this paper, we present an extension of slack analysis for budgeting in the design of weakly-hard real-time systems. During design, it often happens that some parts of a task set are fully specified while other parameters, e.g. regarding recovery or monitoring tasks, will be available only much later. In such cases, slack analysis can help anticipate how these missing parameters can influence the behavior of the whole system so that a resource budget can be allocated to them. It is, however, sufficient in many application contexts to budget these tasks in order to preserve weakly-hard rather than hard guarantees. We thus present an extension of slack analysis for deriving task budgets for systems with hard and weakly-hard requirements. This work is motivated by and validated on a realistic case study inspired by industrial practice

Quantifying the Flexibility of Real-Time Systems

International audienceIn this paper we define the flexibility of a system as its capability to schedule a new task. We present an approach to quantify the flexibility of a system. More importantly, we show that it is possible under certain conditions to identify the task that will directly induce the limitations on a possible software update. If performed at design time, such a result can be used to adjust the system design by giving more slack to the limiting task. We illustrate how these results apply to a simple system

Bounding Deadline Misses in Weakly-Hard Real-Time Systems with Task Dependencies

International audienceReal-time systems with functional dependencies between tasks often require end-to-end (as opposed to task-level) guarantees. For many of these systems, it is even possible to accept the possibility of longer end-to-end delays if one can bound their frequency. Such systems are called weakly-hard. In this paper we provide end-to-end deadline miss models for systems with task chains using Typical Worst-Case Analysis (TWCA). This bounds the number of potential deadline misses in a given sequence of activations of a task chain. To achieve this we exploit task chain properties which arise from the priority assignment of tasks in static-priority preemptive systems. This work is motivated by and validated on a realistic case study inspired by industrial practice and derived synthetic test cases

Assessment of arterial function in pregnancy: recommendations of the International Working Group on Maternal Haemodynamics.

There is strong evidence supporting a role of maternal arterial dysfunction in pregnancy-specific disorders such as pre-eclampsia and intra-uterine growth restriction. As more work is focused towards this field, it is important that methods and interpretation of arterial function assessment are utilised appropriately. Here, we summarise techniques and devices commonly used in maternal health studies, with considerations of technical application within pregnant cohorts

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

L’événement monstrueux dans les récits de H. P. Lovecraft

L’homme, vous le savez, c’est cette créature sonore qui gesticule dans le vaste vide intersidéral. Vous le savez, car Macbeth le disait déjà à sa manière, il y a quelques siècles de cela : la vie, disait-il, « c’est une histoire racontée par un idiot, pleine de bruit et de fureur, qui ne signifie rien ». Quant à moi, je dirai ici des choses simples. L’événement sera – à proprement parler – le cœur de mon propos. D’abord y plonger, puis suivre un des flux qu’il engendre. Le réel est ab..

Une littérature qui ne passe pas. Récits de captivité des prisonniers de guerre français de la Seconde Guerre mondiale (1940-1953)

Just as the narratives of the victims of political and racial internment, the narratives of the French prisoners of war during the Second World War are deeply interesting as far as history, documentation, ideology, but also literature are concerned. Between 1940 and1953, no less than 188 narratives—testimonies, diaries, novels—were published ; they constitute a rich corpus that had never been analysed before. This thesis intends to sort out the different issues that revolve around these narratives, through the study of their political and literary contexts.Tout comme les récits de déportation politique et raciale, les récits de captivité des prisonniers de guerre français de la Seconde Guerre mondiale présentent un intérêt non négligeable, du point de vue historique, documentaire, idéologique, mais aussi littéraire.Entre 1940 et 1953, pas moins de 188 récits — témoignages, journaux, romans — furent publiés, qui constituent un corpus riche qui n'a pas été étudié jusqu'à présent. Cette thèse de doctorat entreprend de démêler, à travers l'étude du contexte littéraire et politique de l'époque, les différents enjeux qui gravitent autour de ces récits

A neural field model of the dynamics of goal-directed eye movements

International audiencePrimates (including humans) heavily rely on the efficiency of their visual system which, contrary to man-made cameras, exploits parallel channels conveying qualitatively different signals with graded precision depending on the eccentricity of objects in the visual field and with different dynamics. The interactions with the visual environment involve selecting and focusing on targets or areas of interest, actively and continuously sampling local information, instead of contemplating a homogeneous visual flow. Eye movements are a specific case of these actions, allowing to foveate targets and track their motion. Once the target is captured in the fovea, these movements are usually classified into catch-up saccades (with the eye rapidly jumping from one orientation – or fixation – to another) and smooth pursuit (with the eye continuously tracking a target with low velocity). Recent results in the monkey show a reduction in the number of catch-up saccades and an increase of smooth pursuit velocity when a moving target is repeatedly observed and tracked [1]. The mechanisms underlying this learning permit the maintenance of the target within in the central visual field at its current (here-and-now) location, despite the delays involved in processing visual signals. Here, we model such transitions and sustained oculomotor response using dynamic neural fields, extending previous models by incorporating actions and activity propagation into the dynamical equations ruling the estimation of current target position [2]. Such estimated position is then used to trigger eye movements, either taking the form of pursuit slow eye movement or a catch-up saccade depending on the location and the dynamical characteristics of activity in the neural field. Propagation of activity in various directions thus compete for action at all times, leading to qualitatively different behaviors: 1) fixations and fixational eye movements due to small variations in the weight of opposite propagations when the target is stationary, 2) intercepting or catch-up saccades when activity peaks alternatively build and relax on the neural field (e.g. when the target moves at a high but unknown velocity), 3) slow eye movement when the activity peak drifts and follows the target movement, 4) pursuit eye movements compensating for lags or change in direction when the target trajectory is learned. Therefore, smoothly pursuing a foveated moving target here simply corresponds to stable attractors in the dynamical oculomotor system, adopting a sensorimotor and interactive view of the visual system. Such stable attractors only appear once learning has extracted the regularities of target trajectories

Active Neural Field model of goal directed eye-movements

International audienceFor primates (including humans), interacting with objects of interest in the environment often involves their foveation, many of them not being static (e.g. other animals, relative motion due to self-induced movement). Eye movements allow the active and continuous sampling of local information, exploiting the graded precision of visual signals (e.g., due to the types and distributions of photoreceptors). Foveating and tracking targets thus requires adapting to their motion. Indeed, considering the delays involved in the transmission of retinal signals to the eye muscles, a purely reactive schema could not account for the smooth pursuit movements which maintain the target within the central visual field. Internal models have been posited to represent the future position of the target (for instance extrapolating from past observations), in order to compensate for these delays. Yet, adaptation of the sensorimotor and neural activity may be sufficient to synchronize with the movement of the target, converging to encoding its location here-and-now, without explicitly resorting to any frame of reference (Goffart et al., 2017).Committing to a distributed dynamical systems approach, we relied on a computational implementation of neural fields to model an adaptation mechanism sufficient to select, focus and track rapidly moving targets. By coupling the generation of eye-movements with dynamic neural field models and a simple learning rule, we replicated neurophysiological results that demonstrated how the monkey adapts to repeatedly observed moving targets (Bourrelly et al., 2016; Quinton & Goffart, 2018), progressively reducing the number of catch-up saccades and increasing smooth pursuit velocity (yet not going beyond the here-and-now target location). We now focus on eye-movements observed in presence of two simultaneously moving centrifugal targets (Goffart, 2016), for which the reduction to a single trajectory with some predicted dynamics (e.g., target center) is even more inappropriate