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In Ecuador, electricity generation is mainly covered by renewable energy sources that feed the National Interconnected System (NIS). Its economical price means that the installation of systems based on non-conventional renewable energy sources does not represent a benefit for the user. However, rural communities isolated from the NIS do not have electricity services. Due to this, isolated systems become an effective option to supply electricity to these communities. In this regard, the Galapagos Islands have unique biodiversity in the world. Since they are not connected to the NIS, their primary energy sources are based on biogas obtained from fossil fuels, with their negative consequences despite the great potential of solar resources. Hence the need to use non-conventional renewable energy sources that covers the energy demand and do not affect the biodiversity there. Photovoltaic energy forecasting is an essential step in the installation of photovoltaic systems. Prediction models based on deep learning (DL) techniques can obtain a high degree of accuracy in energy prediction tasks. For this reason, this work presents the development of long short-term memory (LSTM) and gated recurrent unit (GRU) models to predict photovoltaic energy in an isolated area of Ecuador. The results highlight the performance of both methods through the achieved short-term prediction. © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Peer Reviewed

Neurotechnology applies methods and devices to mitigate the burden of neurological and mental disorders. In particular, neural interfaces establish a long‐term, seamless, symbiotic integration between implants and neural tissue. Materials play a pivotal role in neurotechnology. Advanced materials and materials engineering are crucial to achieving the desired function and outcome. Recently, neural interfaces extended their range of applications with the emergence of flexible, conformable, stretchable, injectable, and transient electronics. However, despite this enormous advancement in materials science and engineering, clinical devices still rely on old‐fashioned but reliable materials and processes. The gap between research development and industry adoption has recently gained high interest. This article analyzes recent developments, discusses roadblocks, and provides a roadmap for materials engineering applied to neurotechnology.

Objective. 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. Approach. 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. Main results. 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. Significance. 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.

Objective. For decades electrical stimulation has been used in neuroscience to investigate brain networks and been deployed clinically as a mode of therapy. Classically, all methods of electrical stimulation require implanted electrodes to be connected in some manner to an apparatus which provides power for the stimulation itself. Approach. We show the use of novel organic electronic devices, specifically organic electrolytic photocapacitors (OEPCs), which can be activated when illuminated with deep-red wavelengths of light and correspondingly do not require connections with external wires or power supplies when implanted at various depths in vivo. Main results. We stimulated cortical brain tissue of mice with devices implanted subcutaneously, as well as beneath both the skin and skull to demonstrate a wireless stimulation of the whisker motor cortex. Devices induced both a behavior response (whisker movement) and a sensory response in the corresponding sensory cortex. Additionally, we showed that coating OEPCs with a thin layer of a conducting polymer formulation (PEDOT:PSS) significantly increases their charge storage capacity, and can be used to further optimize the applied photoelectrical stimulation. Significance. Overall, this new technology can provide an on-demand electrical stimulation by simply using an OEPC and a deep-red wavelength illumination. Wires and interconnects to provide power to implanted neurostimulation electrodes are often problematic in freely-moving animal research and with implanted electrodes for long-term therapy in patients. Our wireless brain stimulation opens new perspectives for wireless electrical stimulation for applications in fundamental neurostimulation and in chronic therapy. Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [716867, 949191]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; Swedish Research Council (Vetenskapsradet)Swedish Research Council [2018-04505]

In Ecuador, electricity generation is mainly covered by renewable energy sources that feed the National Interconnected System (NIS). Its economical price means that the installation of systems based on non-conventional renewable energy sources does not represent a benefit for the user. However, rural communities isolated from the NIS do not have electricity services. Due to this, isolated systems become an effective option to supply electricity to these communities. In this regard, the Galapagos Islands have unique biodiversity in the world. Since they are not connected to the NIS, their primary energy sources are based on biogas obtained from fossil fuels, with their negative consequences despite the great potential of solar resources. Hence the need to use non-conventional renewable energy sources that covers the energy demand and do not affect the biodiversity there. Photovoltaic energy forecasting is an essential step in the installation of photovoltaic systems. Prediction models based on deep learning (DL) techniques can obtain a high degree of accuracy in energy prediction tasks. For this reason, this work presents the development of long short-term memory (LSTM) and gated recurrent unit (GRU) models to predict photovoltaic energy in an isolated area of Ecuador. The results highlight the performance of both methods through the achieved short-term prediction. © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Peer Reviewed

Much is currently being studied on the negative visual impact associated to the installation of large wind turbines or photovoltaic farms. However, methodologies for quantitatively assessing landscape impact are scarce. In this work we used electroencephalographic (EEG) recordings to investigate the brain activity of 14 human volunteers when looking at the same landscapes with and without wind turbines, solar panels and nuclear power plants. Our results showed no significant differences for landscapes with solar power systems or without them, and the same happened for wind turbines, what was in agreement with their subjective scores. However, there were clear and significant differences when looking at landscapes with and without nuclear power plants. These differences were more pronounced around a time window of 376-407 msec and showed a clear right lateralization for the pictures containing nuclear power plants. Although more studies are still needed, these results suggest that EEG recordings can be a useful procedure for measuring visual impact. (C) 2017 Elsevier Ltd. All rights reserved.

Optogenetics allows light activation of genetically defined cell populations and the study of their link to specific brain functions. While it is a powerful method that has revolutionized neuroscience in the last decade, the shortcomings of directly stimulating electrodes and living tissue with light have been poorly characterized. Here, we assessed the photovoltaic effects in local field potential (LFP) recordings of the mouse hippocampus. We found that light leads to several artifacts that resemble genuine LFP features in animals with no opsin expression, such as stereotyped peaks at the power spectrum, phase shifts across different recording channels, coupling between low and high oscillation frequencies, and sharp signal deflections that are detected as spikes. Further, we tested how light stimulation affected hippocampal LFP recordings in mice expressing channelrhodopsin 2 in parvalbumin neurons (PV/ChR2 mice). Genuine oscillatory activity at the frequency of light stimulation could not be separated from light-induced artifacts. In addition, light stimulation in PV/ChR2 mice led to an overall decrease in LFP power. Thus, genuine LFP changes caused by the stimulation of specific cell populations may be intermingled with spurious changes caused by photovoltaic effects. Our data suggest that care should be taken in the interpretation of electrophysiology experiments involving light stimulation.