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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.

 

In this paper, we propose a simplified and robust model for place cell generation based on the oscillatory interference (OI) model concept. Aiming toward hardware implementation in bio-inspired simultaneous localization and mapping (SLAM) systems for mobile robotics, we base our model on logic operations that reduce its computational complexity. The model compensates for parameter variations in the behaviors of the population of constituent theta cells, and allows the theta cells to have square-wave oscillation profiles. The robustness of the model, with respect to mismatch in the theta cell’s base oscillation frequency and gain—as a function of modulatory inputs—is demonstrated. Place cell composed of 48 theta cells with base frequency variations with a 25% standard deviation from the mean and a gain error with 20% standard deviation from the mean only result in a 20% deformations within the place field and 0.24% outer side lobes, and an overall pattern with 0.0015 mean squared error on average. We also present how the model can be used to achieve the localization and path-tracking functionalities of SLAM. Hence, we propose a model for spatial cell formation using theta cells with behaviors that are biologically plausible and hardware implementable for real world application in neurally-inspired SLAM.

 

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. 

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22–24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

 

Most of the next-generation implantable medical devices that are targeting sub-mm scale form factors are entirely powered wirelessly. The most commonly used form of wireless power transfer for ultra-small receivers is inductive coupling and has been so for many decades. This might change with the advent of novel microfabricated magnetoelectric (ME) antennas which are showing great potential as high-frequency wireless powered receivers. In this paper, we compare these two wireless power delivery methods using receivers that operate at 2.52 GHz with a surface area of 0.043 mm2 . Measurement results show that the maximum achievable power transfer of a ME antenna outperforms that of an on-silicon coil by approximately 7 times for a Tx-Rx distance of 2.16 and 3.3 times for a Tx-Rx distance of 0.76 cm. 

Compressive video measurements can save bandwidth and data storage. However, conventional approaches to target detection require the compressive measurements to be reconstructed before any detectors are applied. This is not only time consuming but also may lose information in the reconstruction process. In this paper, we summarized the application of a recent approach to vehicle detection and classification directly in the compressive measurement domain to human targets. The raw videos were collected using a pixel-wise code exposure (PCE) camera, which condensed multiple frames into one frame. A combination of two deep learning-based algorithms (you only look once (YOLO) and residual network (ResNet)) was used for detection and confirmation. Optical and mid-wave infrared (MWIR) videos from a well-known database (SENSIAC) were used in our experiments. Extensive experiments demonstrated that the proposed framework was feasible for target detection up to 1500 m, but target confirmation needs more research. 

Grid, place, and border cells in the mammalian hippocampus and entorhinal cortex perform highly sophisticated navigational tasks with an extremely low power budget. While previous algorithms for simultaneous localization and mapping (SLAM) in robotics have used these cells for inspiration, they have sacrificed the robust, low-power gains achieved with bioplausible models for ease of implementation. This paper presents steps towards robotic navigation with biologically realistic hippocampal models by implementing velocity-controlled oscillators, a basis for any spatially-tuned neuron, on mixed-mode neuromorphic spiking hardware. 

The pixel-wise code exposure (PCE) camera is a compressive sensing camera that has several advantages, such as low power consumption and high compression ratio.Moreover, one notable advantage is the capability to control individual pixel exposure time. Conventional approaches of using PCE cameras involve a time-consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. Otherwise, conventional approaches will fail if compressive measurements are used. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done via detection using You Only Look Once (YOLO), and the classification is achieved using residual network (ResNet). Extensive simulations using short-wave infrared (SWIR) videos demonstrated the efficacy of our proposed approach.