ODEs model of foreign body reaction around peripheral nerve implanted electrode

n/

Perceptual abstraction and attention

This is a report on the preliminary achievements of WP4 of the IM-CleVeR project on abstraction for cumulative learning, in particular directed to: (1) producing algorithms to develop abstraction features under top-down action influence; (2) algorithms for supporting detection of change in motion pictures; (3) developing attention and vergence control on the basis of locally computed rewards; (4) searching abstract representations suitable for the LCAS framework; (5) developing predictors based on information theory to support novelty detection. The report is organized around these 5 tasks that are part of WP4. We provide a synthetic description of the work done for each task by the partners

Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview

Computational approaches for modelling the central nervous system (CNS) aim to develop theories on processes occurring in the brain that allow the transformation of all information needed for the execution of motor acts. Computational models have been proposed in several fields, to interpret not only the CNS functioning, but also its efferent behaviour. Computational model theories can provide insights into neuromuscular and brain function allowing us to reach a deeper understanding of neuroplasticity. Neuroplasticity is the process occurring in the CNS that is able to permanently change both structure and function due to interaction with the external environment. To understand such a complex process several paradigms related to motor learning and computational modeling have been put forward. These paradigms have been explained through several internal model concepts, and supported by neurophysiological and neuroimaging studies. Therefore, it has been possible to make theories about the basis of different learning paradigms according to known computational models. Here we review the computational models and motor learning paradigms used to describe the CNS and neuromuscular functions, as well as their role in the recovery process. These theories have the potential to provide a way to rigorously explain all the potential of CNS learning, providing a basis for future clinical studies

Humans Can Integrate Augmented Reality Feedback in Their Sensorimotor Control of a Robotic Hand

Tactile feedback is pivotal for grasping and manipulation in humans. Providing functionally effective sensory feedback to prostheses users is an open challenge. Past paradigms were mostly based on vibroor electrotactile stimulations. However, the tactile sensitivity on the targeted body parts (usually the forearm) is greatly less than that of the hand/fingertips, restricting the amount of information that can be provided through this channel. Visual feedback is the most investigated technique in motor learning studies, where it showed positive effects in learning both simple and complex tasks; however, it was not exploited in prosthetics due to technological limitations. Here, we investigated if visual information provided in the form of augmented reality (AR) feedback can be integrated by able-bodied participants in their sensorimotor control of a pick-and-lift task while controlling a robotic hand. For this purpose, we provided visual continuous feedback related to grip force and hand closure to the participants. Each variable was mapped to the length of one of the two ellipse axes visualized on the screen of wearable single-eye display AR glasses. We observed changes in behavior when subtle (i.e., not announced to the participants) manipulation of the AR feedback was introduced, which indicated that the participants integrated the artificial feedback within the sensorimotor control of the task. These results demonstrate that it is possible to deliver effective information through AR feedback in a compact and wearable fashion. This feedback modality may be exploited for delivering sensory feedback to amputees in a clinical scenario