A fundamental challenge for bioelectronics is to deliver power to miniature devices inside the body. Wires are common failure points and limit device placement. On the other hand, wireless power by electromagnetic or ultrasound waves must overcome absorption by the body and impedance mismatches between air, bone, and tissue. In contrast, magnetic fields suffer little absorption by the body or differences in impedance at interfaces between air, bone, and tissue. These advantages have led to magneticallypowered stimulators based on induction or magnetothermal effects. However, fundamental limitations in these power transfer technologies have prevented miniature magnetically-powered stimulators from applications in many therapies and disease models because they do not operate in clinical "highfrequency" ranges above 50 Hz. Here we show that magnetoelectric materials -applied in bioelectronic devices -enable miniature magnetically-powered neural stimulators that can operate up to clinically-relevant high-frequencies.As an example, we show that ME neural stimulators can effectively treat the symptoms of a hemi-Parkinson's disease model in freely behaving rodents. We further demonstrate that ME-powered devices can be miniaturized to mmsized devices, fully implanted, and wirelessly powered in freely behaving rodents. These results suggest that ME materials are an excellent candidate for wireless power delivery that will enable miniature bioelectronics for both clinical and research applications.

Observing the activity of large populations of neurons in vivo is critical for understanding brain function and dysfunction. The use of fluorescent genetically-encoded calcium indicators (GECIs) in conjunction with miniaturized microscopes is an exciting emerging toolset for recording neural activity in unrestrained animals. Despite their potential, current miniaturized microscope designs are limited by using image sensors with low frame rates, sensitivity, and resolution. Beyond GECIs, there are many neuroscience applications which would benefit from the use of other emerging neural indicators, such as fluorescent genetically-encoded voltage indicators (GEVIs) that have faster temporal resolution to match neuron spiking, yet, require imaging at high speeds to properly sample the activity-dependent signals. We integrated an advanced CMOS image sensor into a popular open-source miniaturized microscope platform. MiniFAST is a fast and sensitive miniaturized microscope capable of 1080p video, 1.5 μm resolution, frame rates up to 500 Hz and high gain ability (up to 70 dB) to image in extremely low light conditions. We report results of high speed 500 Hz in vitro imaging of a GEVI and ~300 Hz in vivo imaging of transgenic Thy1-GCaMP6f mice. Finally, we show the potential for a reduction in photobleaching by using high gain imaging with ultra-low excitation light power (0.05 mW) at 60 Hz frame rates while still resolving Ca2+ spiking activity. Our results extend miniaturized microscope capabilities in high-speed imaging, high sensitivity and increased resolution opening the door for the open-source community to use fast and dim neural indicators.

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large scale modalities. Here we introduce ghostipy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. ghostipy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis. Significance StatementDue to technological innovation the size of neural recordings has increased dramatically, but downstream analysis code is often not optimized to handle such large scales of data efficiently. Here we have developed GhostiPy, an open source Python package prioritizing 1/25 performance and efficiency for large data in the context of typical spectral analysis and signal processing algorithms. Users can control hardware resource consumption (such as system memory) by setting the level of parallelization and enabling out-of-core processing.Thus algorithms can be run on a variety of hardware, from laptops to dedicated compute servers. Overall, GhostiPy improves experimental throughput by increasing the portability of analyses.

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Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large scale modalities. Here we introduce ghostipy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. ghostipy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.