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Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Holding a handrail or using a cane may decrease the energy cost of walking in stroke survivors. However, the factors underlying this decrease have not yet been previously identified. The purpose of the current study was to fill this void by investigating the effect of physical support (through handrail hold) and/or somatosensory input (through light touch contact with a handrail) on energy cost and accompanying changes in both step parameters and neuromuscular activity. Elucidating these aspects may provide useful insights into gait recovery post stroke.Fifteen stroke survivors participated in this study. Participants walked on a treadmill under three conditions: no handrail contact, light touch of the handrail, and firm handrail hold. During the trials we recorded oxygen consumption, center of pressure profiles, and bilateral activation of eight lower limb muscles. Effects of the three conditions on energy cost, step parameters and neuromuscular activation were compared statistically using conventional ANOVAs with repeated measures. In order to examine to which extent energy cost and step parameters/muscle activity are associated, we further employed a partial least squares regression analysis.Handrail hold resulted in a significant reduction in energy cost, whereas light touch contact did not. With handrail hold subjects took longer steps with smaller step width and improved step length symmetry, whereas light touch contact only resulted in a small but significant decrease in step width. The EMG analysis indicated a global drop in muscle activity, accompanied by an increased constancy in the timing of this activity, and a decreased co-activation with handrail hold, but not with light touch. The regression analysis revealed that increased stride time and length, improved step length symmetry, and decreased muscle activity were closely associated with the decreased energy cost during handrail hold.Handrail hold, but not light touch, altered step parameters and was accompanied by a global reduction in muscle activity, with improved timing constancy. This suggests that the use of a handrail allows for a more economic step pattern that requires less muscular activation without resulting in substantial neuromuscular re-organization. Handrail use may thus have beneficial effects on gait economy after stroke, which cannot be accomplished through enhanced somatosensory input alone.

Indole is a high-toxic refractory nitrogen-containing compound that could cause serious harm to the human and ecosystem. It has been a challenge to develop economical and efficient technology for degrading indole. Microbial fuel cell (MFC) has great potential in the removal of organic pollutants utilizing microorganisms as catalysts to degrade organic matter into the nutrients. Herein, a novel anode of Fe2O3-polyaniline-dopamine hybrid composite modified carbon felt (Fe2O3-PDHC/CF) was prepared by electrochemical deposition. The degradation efficiency of indole by the MFC loading Fe2O3-PDHC/CF anode was up to 90.3 % in 120 h operation, while that of the MFC loading CF anode was only 44.0 %. The maximum power density of the MFC loading Fe2O3-PDHC/CF anode was 3184.4 mW·m-2, increasing 113 % compared to the MFC loading CF anode. The superior performances of the MFC with Fe2O3-PDHC surface-modified anode owned to the synergistic effect of high conductive Fe2O3 and admirably biocompatible polyaniline-dopamine. MFC with the Fe2O3-PDHC/CF anode could produce considerable electricity and effectively degrade indole in water, which demonstrated a practical approach for the efficient degradation of refractory organic compounds in wastewater.

The simulation of detailed neuronal circuits is based on computationally expensive software simulations and requires access to a large computing cluster. The appearance of new Instruction Set Architectures (ISAs) in most recent High-Performance Computing (HPC) systems, together with the layers of system software and complex scientific applications running on top of them, makes the performance and power figures challenging to evaluate. In this paper, we focus on evaluating CoreNEURON on two HPC systems powered by Intel and Arm architectures. CoreNEURON is a computational engine of the widely used NEURON simulator adapted to run on emerging architectures while maintaining compatibility with existing NEURON models developed by the neuroscience community. The evaluation is based on the analysis of the dynamic instruction mix on two versions of CoreNEURON. It focuses on the performance gain obtained by exploiting the Single Instruction Multiple Data (SIMD) unit and includes energy measurements. Our results show that using a tool for increasing data-level parallelism (ISPC) boosts the performance up to 2× independently on the ISA. Its combination with vendor-specific compilers can further speed up the neural simulation time. Also, the performance/price ratio is higher for Arm-based systems than for Intel ones making them more cost-efficient keeping the same usability level of other HPC systems. This work is partially supported by the Spanish Government (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P), by the Generalitat de Catalunya (2017-SGR-1414), by the European Mont-Blanc 3 project (GA n. 671697), Human Brain Project SGA2 (GA n. 785907), and POP CoE (GA n. 824080). Peer Reviewed

Like human walking, passive dynamic walking-i.e. walking down a slope with no actuation except gravity-is energy efficient by exploiting the natural dynamics. In the animal world, neural oscillators termed central pattern generators (CPGs) provide the basic rhythm for muscular activity in locomotion. We present a CPG model, which automatically tunes into the resonance frequency of the passive dynamics of a bipedal walker, i.e. the CPG model exhibits resonance tuning behavior. Each leg is coupled to its own CPG, controlling the hip moment of force. Resonance tuning above the endogenous frequency of the CPG-i.e. the CPG's eigenfrequency-is achieved by feedback of both limb angles to their corresponding CPG, while integration of the limb angles provides resonance tuning at and below the endogenous frequency of the CPG. Feedback of the angular velocity of both limbs to their corresponding CPG compensates for the time delay in the loop coupling each limb to its CPG. The resonance tuning behavior of the CPG model allows the gait velocity to be controlled by a single parameter, while retaining the energy efficiency of passive dynamic walking.

AbstractBiofuel cell (BFC) that transfers chemical energy into electricity is a promising candidate as an energy‐harvesting device for implantable electronics. However, there still remain major challenges for implantable BFCs, including bulky and rigid device structure mismatching with soft tissues such as the brain, and the power output decreases due to the fouling process in a biological environment. Here, a flexible and anti‐biofouling fiber BFC working in the brain chronically is developed. The fiber BFC is based on a carbon nanotube fiber electrode to possess small size and flexibility. A hydrophilic zwitterionic anti‐biofouling polydopamine‐2‐methacryloyloxyethyl phosphorylcholine layer is designed on the surface of fiber BFC to resist the nonspecific protein adsorption in a complex biological environment. After implantation, the fiber BFC can achieve a stable device/tissue interface, along with a negligible immune response. The fiber BFC has first realized power generation in the mouse brain for over a month, exhibiting its promising prospect as an energy‐harvesting device in vivo.

Wireless control and power harvesting systems that operate injectable, cellular-scale optoelectronic components provide important demonstrated capabilities in neuromodulatory techniques such as optogenetics. Here, we report a radio frequency (RF) control/harvesting device that offers dramatically reduced size, decreased weight and improved efficiency compared to previously reported technologies. Combined use of this platform with ultrathin, multijunction, high efficiency solar cells allows for hundred-fold reduction of transmitted RF power, which greatly enhances the wireless coverage.Fabrication involves separate construction of the harvester and the injectable µ-ILEDs. To test whether the presence of the implantable device alters behavior, we implanted one group of wild type mice and compared sociability behavior to unaltered controls. Social interaction experiments followed protocols defined by Silverman et al. with minor modifications.The results presented here demonstrate that miniaturized RF harvesters, and RF control strategies with photovoltaic harvesters can, when combined with injectable µ-ILEDs, offer versatile capabilities in optogenetics. Experimental and modeling studies establish a range of effective operating conditions for these two approaches. Optogenetics studies with social groups of mice demonstrate the utility of these systems.The addition of miniaturized, high performance photovoltaic cells significantly expands the operating range and reduces the required RF power. The platform can offer capabilities to modulate signaling path in the brain region of freely-behaving animals. These suggest its potential for widespread use in neuroscience.