Understanding sensory induced cortical patterns in the primary visual cortex V1 is an important challenge both for physiological motivations and for improving our understanding of human perception and visual organisation. In this work we focus on pattern formation in the visual cortex when the cortical activity is driven by a geometric visual hallucination-like stimulus. In particular, we present a theoretical framework for sensory induced hallucinations which allows one to reproduce novel psychophysical results such as the MacKay effect (Nature, 1957) and the Billock and Tsou experiences (PNAS, 2007).

Research in the field of the powered orthoses or exoskeletons has expanded tremendously over the past years. Lower limb exoskeletons are widely used in robotic rehabilitation and are showing benefits in the patients quality of life. Many engineering reviews have been published about these devices and addressed general aspects. To the best of our knowledge, no review has minutely discussed specifically the control of the most common used devices, particularly the algorithms used to define the function state of the exoskeleton, such as walking, sit-to-stand, etc. In this contribution, the control hardware and software, as well as the integrated sensors for the feedback are thoroughly analyzed. We will also discuss the importance of user-specific state definition and customized control architecture. Although there are many prototypes developed nowadays, we chose to target medical lower limb exoskeletons that uses crutches to keep balance, and that are minimally actuated. These are the most common system that are now being commercialized and used worldwide. Therefore, the outcome of such a review helps to have a practical insight in all of: the mechatronics design, system architecture, and control technology.

Neurons are equipped with homeostatic mechanisms that counteract long-term perturbations of their average activity and thereby keep neurons in a healthy and information-rich operating regime. While homeostasis is believed to be crucial for neural function, a systematic analysis of homeostatic control has largely been lacking. The analysis presented here analyses the necessary conditions for stable homeostatic control. We consider networks of neurons with homeostasis and show that homeostatic control that is stable for single neurons, can destabilize activity in otherwise stable recurrent networks leading to strong non-abating oscillations in the activity. This instability can be prevented by slowing down the homeostatic control. The stronger the network recurrence, the slower the homeostasis has to be. Next, we consider how non-linearities in the neural activation function affect these constraints. Finally, we consider the case that homeostatic feedback is mediated via a cascade of multiple intermediate stages. Counter-intuitively, the addition of extra stages in the homeostatic control loop further destabilizes activity in single neurons and networks. Our theoretical framework for homeostasis thus reveals previously unconsidered constraints on homeostasis in biological networks, and identifies conditions that require the slow time-constants of homeostatic regulation observed experimentally.

In this letter, we propose an alternative proof for the uniqueness of Maronna's M-estimator of scatter for N vector observations y1, ..., yN∈ Rmunder a mild constraint of linear independence of any subset of m of these vectors. This entails in particular almost sure uniqueness for random vectors yi with a density as long as N > m. This approach allows to establish further relations that demonstrate that a properly normalized Tyler's M-estimator of scatter can be considered as a limit of Maronna's M-estimator. More precisely, the contribution is to show that each M-estimator, verifying some mild conditions, converges towards a particular Tyler's M-estimator. These results find important implications in recent works on the large dimensional (random matrix) regime of robust M-estimation.

In this paper, we consider the control system defined by the rolling of a strictly convex surface S of IR3on a plane without slipping or spinning. The purpose of this paper is to present the numerical implementation of a constructive planning algorithm for Σ, which is based on a continuation method. The performances of that algorithm, both in robustness and convergence speed, are illustrated through several examples.

The nonholonomy exhibited by kinematic systems consisting of bodies rolling on top of each other can be used to the purpose of building dexterous mechanism with a minimum hardware complication. Previous work concentrated on manipulation of objects possessing a regular surface. On the other hand, industrial parts are most often irregular, possessing vertices and edges. In this paper we present some results on the description of the set of positions and orientations that polyhedral objects can reach when manipulated by rolling without slipping. An algorithm for planning the manipulation of a polyhedral part from a given configuration to another reachable one, is also presented.