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Abstract A neuromorphic simultaneous localization and mapping (SLAM) system shows potential for more efficient implementation than its traditional counterpart. At the mean time a neuromorphic model of spatial encoding neurons in silicon could provide insights on the functionality and dynamic between each group of cells. Especially when realistic factors including variations and imperfections on the neural movement encoding are presented to challenge the existing hypothetical models for localization. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, egocentric place cells, and the typical allocentric place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells observed in rodents. The system consists of a theta chip with 128 theta cell units and an FPGA implementing 4 networks for egocentric place cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from as much as 18% deviation, induced by parameter variations in analog circuits, from the mathematical model of theta cells. We provide a model for implementing dynamic neuromorphic SLAM systems for dynamic-scale mapping of cluttered environments, even when subject to significant errors in sensory measurements and real-time analog computation. We also suggest a robust approach for the network topology of spatial cells that can mitigate neural non-uniformity and provides a hypothesis for the function of grid cells and the existence of egocentric place cells. 

Abstract—Accurately capturing dynamic scenes with wideranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera’s frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a monochrome pixel-wise programmable image sensor, our sampling pattern captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds — both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system’s adaptability and performance in dynamic conditions. Index Terms—High-dynamic-range video, high-speed imaging, CMOS image sensors, programmable sensors, deep learning, convolutional neural networks. 

Abstract In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility. 

Realizing Hebbian plasticity in large-scale neuromorphic systems is essential for reconfiguring them for recognition tasks. Spike-timing-dependent plasticity, as a tool to this effect, has received a lot of attention in recent times. This phenomenon encodes weight update information as correlations between the presynaptic and postsynaptic event times, as such, it is imperative for each synapse in a silicon neural network to somehow keep its own time. We present a biologically plausible and optimized Register Transfer Level (RTL) and algorithmic approach to the Nearest-Neighbor STDP with time management handled by the postsynaptic dendrite. We adopt a time-constant based ramp approximation for ease of RTL implementation and incorporation in large-scale digital neuromorphic systems. 

Abstract Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.