Vittorio Sanguineti

Connecting brains to robots: an artificial body for studying the computational properties of neural tissues

We have created a hybrid neuro-robotic system that establishes two-way communication between the brain of a lamprey and a small mobile robot. The purpose of this system is to offer a new paradigm for investigating the behavioral, computational, and neurobiological mechanisms of sensory-motor learning in a unified context. The mobile robot acts as an artificial body that delivers sensory information to the neural tissue and receives command signals from it. The sensory information encodes the intensity of light generated by a fixed source. The closed-loop interaction between brain and robot generates autonomous behaviors whose features are strictly related to the structure and operation of the neural preparation. We provide a detailed description of the hybrid system, and we present experimental findings on its performance. In particular, we found (a) that the hybrid system generates stable behaviors, (b) that different preparations display different but systematic responses to the presentation of an optical stimulus, and (c) that alteration of the sensory input leads to short- and long-term adaptive changes in the robot responses. The comparison of the behaviors generated by the lampreys brain stem with the behaviors generated by network models of the same neural system provides us with a new tool for investigating the computational properties of synaptic plasticity.

Self-organizing body schema for motor planning

Abstract This article presents a distributed computational architecture for the motor planning functions of the posterior parietal cortex, which is organized as a computational map and combines a paradigm of self-organization (for building robust and coherent maps of the different motor spaces) with an attractor dynamics (for run-time integration of task constraints). The model, named SO-BoS (self-organizing body-schema), is illustrated with simple simulation results.

Connecting neurons to a mobile robot: an in vitro bidirectional neural interface

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

Self-adaptive robot training of stroke survivors for continuous tracking movements

BackgroundAlthough robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.MethodsThe haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1) a force field generator that combines a non linear attractive field and a viscous field; 2) a performance evaluation module; 3) an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control.ResultsThe preliminary results with a small group of patients (10 chronic hemiplegic subjects) show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients.ConclusionsThe experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale controlled clinical trials. Moreover, the study suggests that including continuous movement in the repertoire of training is acceptable also by rather severely impaired subjects and confirms the stabilizing effect of alternating vision/no vision trials already found in previous studies.

Robotic assessment of upper limb motor function after stroke.

ABSTRACTTraditional assessment of a stroke subject’s motor ability, carried out by a therapist who observes and rates the subject’s motor behavior using ordinal measurements scales, is subjective, time consuming and lacks sensitivity. Rehabilitation robots, which have been the subject of intense inquiry over the last decade, are equipped with sensors that are used to develop objective measures of motor behaviors in a semiautomated way during therapy. This article reviews the current contributions of robot-assisted motor assessment of the upper limb. It summarizes the various measures related to movement performance, the models of motor recovery in stroke subjects and the relationship of robotic measures to standard clinical measures. It analyses the possibilities offered by current robotic assessment techniques and the aspects to address to make robotic assessment a mainstream motor assessment method.

Abnormal sensorimotor control, but intact force field adaptation, in multiple sclerosis subjects with no clinical disability

In MS subjects with no clinical disability, we assessed sensorimotor organization and their ability to adapt to an unfamiliar dynamical environment. Eleven MS subjects performed reaching movements while a robot generated a speed-dependent force field. Control and adaptation performance were compared with that of an equal number of control subjects. During a familiarization phase, when the robot generated no forces, the movements of MS subjects were more curved, displayed greater and more variable directional errors and a longer deceleration phase. During the force field phase, both MS and control subjects gradually learned to predict the robot-generated forces. The rates of adaptation were similar, but MS subjects showed a greater variability in responding to the force field. These results suggest that MS subjects have a preserved capability of learning to predict the effects of the forces, but make greater errors when actually using such predictions to generate movements. Inaccurate motor commands are then compensated later in the movement through an extra amount of sensory-based corrections. This indicates that early in the disease MS subjects have intact adaptive capabilities, but impaired movement execution. Multiple Sclerosis 2008; 14: 330—342. http://msj.sagepub.com

Minimally assistive robot training for proprioception enhancement

In stroke survivors, motor impairment is frequently associated with degraded proprioceptive and/or somatosensory functions. Here we address the question of how to use robots to improve proprioception in these patients. We used an ‘assist-as-needed’ protocol, in which robot assistance was kept to a minimum and was continuously adjusted during exercise. To specifically train proprioceptive functions, we alternated blocks of trials with and without vision. A total of nine chronic stroke survivors participated in the study, which consisted of a total of ten 1-h exercise sessions. We used a linear mixed-effects statistical model to account for the effects of exercise, vision and the degree of assistance on the overall performance, and to capture both the systematic effects and the individual variations. Although there was not always a complete recovery of autonomous movements, all subjects exhibited an increased amount of voluntary control. Moreover, training with closed eyes appeared to be beneficial for patients with abnormal proprioception. Our results indicate that training by alternating vision and no-vision blocks may improve the ability to use proprioception as well as the ability to integrate it with vision. We suggest that the approach may be useful in the more general case of motor skill acquisition, in which enhancing proprioception may improve the ability to physically interact with the external world.

Bio-mimetic trajectory generation of robots via artificial potential field with time base generator

This paper proposes a new trajectory generation method that allows full control of transient behavior, namely, time-to-target and velocity profile, based on the artificial potential field approach for a real-time robot motion planning problem. Little attention, in fact, has been paid to the temporal aspects of this class of path planning methods. The ability to control the motion time to the target as well as the velocity profile of the generated trajectories, however, is of great interest in real-life applications. In the paper, we argue that such transient behavior should be taken into account within the framework of the artificial potential field approach.

New perspectives on the dialogue between brains and machines

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.

Towards an embodied in vitro electrophysiology : the neurobit project

Note: 316 Reference SAMLAB-ARTICLE-2004-002 Record created on 2009-05-12, modified on 2016-08-08