A Spike-Based Neuromorphic Architecture of Stereo Vision.

The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature and yet it is still a critical bottleneck for artificial machine vision systems. As temporal information is a crucial feature in this process, the advent of event-based vision sensors and dedicated event-based processors promises to offer an effective approach to solving the stereo matching problem. Indeed, event-based neuromorphic hardware provides an optimal substrate for fast, asynchronous computation, that can make explicit use of precise temporal coincidences. However, although several biologically-inspired solutions have already been proposed, the performance benefits of combining event-based sensing with asynchronous and parallel computation are yet to be explored. Here we present a hardware spike-based stereo-vision system that leverages the advantages of brain-inspired neuromorphic computing by interfacing two event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor. We describe a prototype interface designed to enable the emulation of a stereo-vision system on neuromorphic hardware and we quantify the stereo matching performance with two datasets. Our results provide a path toward the realization of low-latency, end-to-end event-based, neuromorphic architectures for stereo vision.

Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network.

An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associated with the electrical changes generated by the activity of the motor neurons. Typically, to decode the muscular activation during different movements, a large number of individual motor neurons are monitored simultaneously, producing large amounts of data to be transferred and processed by the computing devices. In this paper, we follow an alternative approach that can be deployed locally on the sensor side. We propose a neuromorphic implementation of a spiking neural network (SNN) to extract spatio-temporal information of EMG signals locally and classify hand gestures with very low power consumption. We present experimental results on the input data stream using a mixed-signal analog/digital neuromorphic processor. We performed a thorough investigation on the performance of the SNN implemented on the chip, by: first, calculating PCA on the activity of the silicon neurons at the input and the hidden layers to show how the network helps in separating the samples of different classes; second, performing classification of the data using state-of-the-art SVM and logistic regression methods and a hardware-friendly spike-based read-out. The traditional algorithm achieved a classification rate of [Formula: see text] and [Formula: see text], respectively, and the spiking learning method achieved [Formula: see text]. The power consumption of the SNN is [Formula: see text], showing the potential of this approach for ultra-low power processing.

Supplemental vibrotactile feedback of real-time limb position enhances precision of goal-directed reaching.

We examined vibrotactile stimulation as a form of supplemental limb state feedback to enhance planning and ongoing control of goal-directed movements. Subjects wore a two-dimensional vibrotactile display on their nondominant arm while performing horizontal planar reaching with the dominant arm. The vibrotactile display provided feedback of hand position such that small hand displacements were more easily discriminable using vibrotactile feedback than with intrinsic proprioceptive feedback. When subjects relied solely on proprioception to capture visuospatial targets, performance was degraded by proprioceptive drift and an expansion of task space. By contrast, reach accuracy was enhanced immediately when subjects were provided vibrotactile feedback and further improved over 2 days of training. Improvements reflected resolution of proprioceptive drift, which occurred only when vibrotactile feedback was active, demonstrating that benefits of vibrotactile feedback are due, in part to its integration into the ongoing control of movement. A partial resolution of task space expansion persisted even when vibrotactile feedback was inactive, demonstrating that training with vibrotactile feedback also induced changes in movement planning. However, the benefits of vibrotactile feedback come at a cognitive cost. All subjects adopted a stereotyped strategy wherein they attempted to capture targets by moving first along one axis of the vibrotactile display and then the other. For most subjects, this inefficient approach did not resolve over two bouts of training performed on separate days, suggesting that additional training is needed to integrate vibrotactile feedback into the planning and online control of goal-directed reaching in a way that promotes smooth and efficient movement. NEW & NOTEWORTHY A two-dimensional vibrotactile display provided state (not error) feedback to enhance control of a moving limb. Subjects learned to use state feedback to perform blind reaches with accuracy and precision exceeding that attained using intrinsic proprioception alone. Feedback utilization incurred substantial cognitive cost: subjects moved first along one axis of the vibrotactile display, then the other. This stereotyped control strategy must be overcome if vibrotactile limb state feedback is to promote naturalistic limb movements.

Effect of Dual Tasking on Vibrotactile Feedback Guided Reaching - a Pilot Study.

Vibrotactile feedback (VTF) has been proposed as a non-invasive way to augment impaired or lost kinesthetic feedback in certain patient populations, thereby enhancing the real-time control of purposeful limb movements and quality of life. We used a dual tasking scenario to investigate the effects of cognitive load and short-term VTF training on VTF-guided reaching. Participants grasped the handle of a planar manipulandum with one hand and received VTF of its motion via a vibrotactile display attached to the non-moving arm. We asked participants to simultaneously perform VTF-guided reaching and a choice reaction time task both before and after training with VTF-guided reaching. Participants readily used VTF to guide goal-directed hand movements in the absence of visual feedback in the dual-task setting, even prior to training. This capability came at the cost of increased movement completion time. Short-term training on VTF-guided reaching induced significant improvements in target capture errors. Pre- and post-training comparisons of dual-task performance found training-related improvements in VTF-guided reach accuracy were resistant to dual-task interference. We found no training-related improvements in movement completion time or button press performance. These results indicate that VTF can be used to complete goal-directed reaches in a dual task situation, and that a single short bout of training sufficed for participants to begin the transition between the cognitive and associative phases of learning for the integration of VTF into the planning and ongoing control of reaching movements.