Interference between postural control and mental task performance in patients with vestibular disorder and healthy controls

OBJECTIVES - To determine whether interference between postural control and mental task performance in patients with balance system impairment and healthy subjects is due to general capacity limitations, motor control interference, competition for spatial processing resources, or a combination of these.METHOD - Postural stability was assessed in 48 patients with vestibular disorder and 24 healthy controls while they were standing with eyes closed on (a) a stable and (b) a moving platform. Mental task performance was measured by accuracy and reaction time on mental tasks, comprising high and low load, spatial and non-spatial tasks. Interference between balancing and performing mental tasks was assessed by comparing baseline (single task) levels of sway and mental task performance with levels while concurrently balancing and carrying out mental tasks.RESULTS - As the balancing task increased in difficulty, reaction times on both low load mental tasks grew progressively longer and accuracy on both high load tasks declined in patients and controls. Postural sway was essentially unaffected by mental activity in patients and controls.CONCLUSIONS - It is unlikely that dual task interference between balancing and mental activity is due to competition for spatial processing resources, as levels of interference were similar in patients with vestibular disorder and healthy controls, and were also similar for spatial and non-spatial tasks. Moreover, the finding that accuracy declined on the high load tasks when balancing cannot be attributed to motor control interference, as no motor control processing is involved in maintaining accuracy of responses. Therefore, interference between mental activity and postural control can be attributed principally to general capacity limitations, and is hence proportional to the attentional demands of both tasks

Interference between postural control and mental task performance in patients with vestibular disorder and healthy controls

Objectives: To determine whether interference between postural control and mental task performance in patients with balance system impairment and healthy subjects is due to general capacity limitations, motor control interference, competition for spatial processing resources, or a combination of these. Method: Postural stability was assessed in 48 patients with vestibular disorder and 24 healthy controls while they were standing with eyes closed on (a) a stable and (b) a moving platform. Mental task performance was measured by accuracy and reaction time on mental tasks, comprising high and low load, spatial and non-spatial tasks. Interference between balancing and performing mental tasks was assessed by comparing baseline (single task) levels of sway and mental task performance with levels while concurrently balancing and carrying out mental tasks. Results: As the balancing task increased in difficulty, reaction times on both low load mental tasks grew progressively longer and accuracy on both high load tasks declined in patients and controls. Postural sway was essentially unaffected by mental activity in patients and controls. Conclusions: It is unlikely that dual task interference between balancing and mental activity is due to competition for spatial processing resources, as levels of interference were similar in patients with vestibular disorder and healthy controls, and were also similar for spatial and nonspatial tasks. Moreover, the finding that accuracy declined on the high load tasks when balancing cannot be attributed to motor control interference, as no motor control processing is involved in maintaining accuracy of responses. Therefore, interference between mental activity and postural control can be attributed principally to general capacity limitations, and is hence proportional to the attentional demands of both tasks

Committee Machines—A Universal Method to Deal with Non-Idealities in RRAM-Based Neural Networks

Artificial neural networks (ANNs) are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Recent years have seen an emergence of research in hardware that strives to break the bottleneck of von Neumann architecture and optimise the data flow; namely to bring memory and computing closer together. One of the most often suggested solutions is the physical implementation of ANNs in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory (RRAM). However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science -- committee machine (CM) -- in the context of RRAM-based neural networks. Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities, random telegraph noise, cycle-to-cycle variability and line resistance. Importantly, we show that the accuracy can be improved even without increasing the number of devices

Structural changes and conductance thresholds in metal-free intrinsic SiOx resistive random access memory

We present an investigation of structural changes in silicon-rich silicon oxide metal-insulator-metal resistive RAM devices. The observed unipolar switching, which is intrinsic to the bulk oxide material and does not involve movement of metal ions, correlates with changes in the structure of the oxide. We use atomic force microscopy, conductive atomic force microscopy, x-ray photoelectron spectroscopy, and secondary ion mass spectroscopy to examine the structural changes occurring as a result of switching. We confirm that protrusions formed at the surface of samples during switching are bubbles, which are likely to be related to the outdiffusion of oxygen. This supports existing models for valence-change based resistive switching in oxides. In addition, we describe parallel linear and nonlinear conduction pathways and suggest that the conductance quantum, G0, is a natural boundary between the high and low resistance states of our devices

Committee machines -- a universal method to deal with non-idealities in memristor-based neural networks

Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science -- committee machines -- in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.Comment: 22 pages, 18 figures, 4 table

Simulation of Inference Accuracy Using Realistic RRAM Devices

Resistive Random Access Memory (RRAM) is a promising technology for power efficient hardware in applications of artificial intelligence (AI) and machine learning (ML) implemented in non-von Neumann architectures. However, there is an unanswered question if the device non-idealities preclude the use of RRAM devices in this potentially disruptive technology. Here we investigate the question for the case of inference. Using experimental results from silicon oxide (SiOx) RRAM devices, that we use as proxies for physical weights, we demonstrate that acceptable accuracies in classification of handwritten digits (MNIST data set) can be achieved using non-ideal devices. We find that, for this test, the ratio of the high- and low-resistance device states is a crucial determinant of classification accuracy, with ~96.8% accuracy achievable for ratios >3, compared to ~97.3% accuracy achieved with ideal weights. Further, we investigate the effects of a finite number of discrete resistance states, sub-100% device yield, devices stuck at one of the resistance states, current/voltage non-linearities, programming non-linearities and device-to-device variability. Detailed analysis of the effects of the non-idealities will better inform the need for the optimization of particular device properties

Committee Machines—A Universal Method to Deal with Non-Idealities in Memristor-Based Neural Networks

Arti ficial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artifi cial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science|committee machines|in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors

The interplay between structure and function in redox-based resistance switching

We report a study of the relationship between oxide microstructure at the scale of tens of nanometres and resistance switching behaviour in silicon oxide. In the case of sputtered amorphous oxides, the presence of columnar structure enables efficient resistance switching by providing an intial structured distribution of defects that can act as precursors for the formation of chains of conductive oxygen vacancies under the application of appropriate electrical bias. Increasing electrode interface roughness decreases electroforming voltages and reduces the distribution of switching voltages. Any contribution to these effects from field enhancement at rough interfaces is secondary to changes in oxide microstructure templated by interface structure

Head-Jolting Nystagmus Occlusion of the Horizontal Semicircular Canal Induced by Vigorous Head Shaking

Importance: We report a new syndrome, which we are calling head-jolting nystagmus, that expands the differential diagnosis of head movement–induced paroxysmal vertigo. Observations: Two male patients (65 and 58 years old) described rotational vertigo after violent and brief (1- to 2-second) oscillations of the head (head jolting) that triggered intense horizontal nystagmus lasting 45 seconds. Accelerations of the head required to induce these episodes could only be achieved by the patients themselves. In case 1, the episodes gradually disappeared over a 6-year period. In case 2, magnetic resonance imaging (3-T) suggested a filling defect within the left horizontal semicircular canal. He underwent surgical canal plugging in March 2013 that resolved the symptoms. Conclusions and Relevance: We attribute head-jolting nystagmus to dislodged material within the horizontal semicircular canal and provide a mechanistic model to explain its origin

Actin and Type I Collagen Propeptide Distribution in the Developing Chick Cornea

PURPOSE. To determine the organization of actin filaments and distribution of type I procollagen during the development of the chick corneal stroma. METHODS. Embryonic chicken corneas of ages 6 to 18 days and 18 days posthatch were cryosectioned and fluorescently labeled for filamentous actin with phalloidin and for the N-and C-terminal propeptides of type I procollagen with specific monoclonal antibodies. Tissue sections were examined by fluorescence and confocal microscopy. RESULTS. Prominent actin filament bundles were present at all embryonic stages, arranged in orthogonal arrays. Type I collagen propeptides were also present, with the C-propeptide visible as small foci, often associated with the actin label. The N-propeptide was also detected in the stromal matrix, especially in Bowman's layer. Actin filaments were also prominent in the corneal epithelium, along with collagen propeptide labeling, up to embryonic day14. CONCLUSIONS. Actin filament bundles are abundant in the stroma, presumably in the keratocytes of the developing chick cornea, and are arranged in an orthogonal manner suggesting a possible role in cell and matrix organization in this tissue. Filament bundles appear to be closely associated with the foci of type I procollagen label, suggesting a possible association between the actin cytoskeleton and the trafficking of collagen. The presence of the N-propeptide of type I collagen in the extracellular matrix and the restricted distribution of the Cpropeptide suggest differential processing of these molecules after secretion. The persistence of the N-propeptide implies a role in development, possibly in association with control of collagen fibril diameter and spacing. (Invest Ophthalmol Vis Sci