Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals

Discrimination in lexical decision.

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently

Recurrent network dynamics reconciles visual motion segmentation and integration

In sensory systems, a range of computational rules are presumed to be implemented by neuronal subpopulations with different tuning functions. For instance, in primate cortical area MT, different classes of direction-selective cells have been identified and related either to motion integration, segmentation or transparency. Still, how such different tuning properties are constructed is unclear. The dominant theoretical viewpoint based on a linear-nonlinear feed-forward cascade does not account for their complex temporal dynamics and their versatility when facing different input statistics. Here, we demonstrate that a recurrent network model of visual motion processing can reconcile these different properties. Using a ring network, we show how excitatory and inhibitory interactions can implement different computational rules such as vector averaging, winner-take-all or superposition. The model also captures ordered temporal transitions between these behaviors. In particular, depending on the inhibition regime the network can switch from motion integration to segmentation, thus being able to compute either a single pattern motion or to superpose multiple inputs as in motion transparency. We thus demonstrate that recurrent architectures can adaptively give rise to different cortical computational regimes depending upon the input statistics, from sensory flow integration to segmentation

Fuel Cells and Batteries In Silico Experimentation Through Integrative Multiscale Modeling

International audienceDevices for electrochemical energy conversion and storage exist at different levels of development, from the early stages of R&D to mature and deployed technologies. Thanks to the very significant progresses achieved in the field of computational science over the past few decades, multiscale modeling and numerical simulation are emerging as powerful tools for in silico studies of mechanisms and processes in these devices. These innovative approaches allow linking the chemical/microstructural properties of materials and components with their macroscopic efficiency. In combination with dedicated experiments, they can potentially provide tremendous progress in designing and optimizing the next-generation electrochemical cells. This chapter provides a comprehensive overview of the theory and practical aspects of integrative multiscale modeling tools within the context of fuel cells and rechargeable batteries. Additionally, the chapter discusses technical dreams and methodological challenges that computational science is facing today in order to help developing efficient, durable, and low-cost electrochemical energy devices but also to trigger major technological breakthroughs

Synergistic Effect of Sulfide and Ammonia on Anaerobic Digestion of Chicken Manure

The effect of the sulfur load on anaerobic digestion of chicken manure (CM) was investigated in a laboratory scale anaerobic mono-digester at high total ammonia nitrogen (TAN) concentrations. The digester was operated for 268 days by increasing the organic loading rate from 0.5 to 2.5kg-VS/m(3)/day and the total Kjeldahl nitrogen up to 5050mg/l. The CH4 yield of 0.36 +/- 0.02m(3)/kg-VS was achieved at 2.5kg-VS/m(3)/day of loading rate without any inhibition. The results showed that, anaerobic mono-digestion of chicken manure was applicable with the acclimation of microbial consortium to high TAN concentrations. However, when the sulfur content of the CM fed to the digester increased suddenly by coincidence, the CH4 yield decreased about 25\% from 0.36 +/- 0.02 to 0.27 +/- 0.03m(3)/kg-VS. As a result, the acetic acid concentration increased from 130 to 1700mg/l showing that the acetate consuming methanogens were detrimentally affected from TAN and total sulfide concentrations above 4000 and 100mg/l, respectively