17-βEstradiol (E2) and dopamine (DA) are biologically significant molecules, with E2 playing critical roles in female sexual development and neuroprotection, and DA being essential for cognitive processes and motor functions. The co-detection of these molecules is of particular interest due to their potential interactions and combined effects on neurological health. Understanding the interactions between E2 and DA is therefore essential to elucidate E2 neuromodulation, but current detection techniques are limited. Fast-scan cyclic voltammetry (FSCV) is a simple and effective electrochemical method that allows for real-time codetection of neurotransmitters and hormones. In this study, FSCV parameters are optimized for the simultaneous electrochemical co-detection of E2 and DA, using carbon fiber microelectrodes (CFMEs) for their high spatial resolution and biocompatibility. The results reveal that E2 exhibits unique electrochemical behavior which can be distinguished from DA through optimized FSCV settings, allowing for their simultaneous detection. Redox interactions altering the DA measurements are also suggested, possibly owing to the antioxidant properties of E2. This study not only enhances the understanding of the electrochemical properties of E2 and DA but also demonstrates the potential of FSCV in investigating their interactions and roles in neuroprotection and oxidative stress. Understanding the chemical interactions between these two species is also critical to guarantee accurate FSCV measurements, especially as FSCV is being increasingly considered to provide neurochemical feedback in AI-based, closed-loop neuroelectronics.

The quest for visual rehabilitation via retinal implant technology remains a complex, multi-dimensional endeavor. Retinal implants are biomedical devices surgically introduced into the ocular region. They propose a novel treatment for degenerative retinal pathologies. One of the most challenging aspects is replicating the retina’s natural scene encoding. Many investigative teams have consistently pursued strategies to overcome this intricate challenge. Still, no one has effectively generated a device mirroring 20/20 vision. This paper presents a proof-of-concept framework that models the computational circuitry of the human retina using spiking neural networks (SNNs) and implements the model on a mixed-signal application-specific integrated circuit (ASIC) employing leaky integrate-and-fire (LIF) neurons. The proposed neuromorphic system on chip (NeuroSoC) demonstrates energy and area efficiency, achieving an energy consumption of approximately 25 pJ per synaptic operation and operating within an active silicon area of about 1 mm square. A detailed characterization of the chip is provided, including measurements of energy consumption per inference, an inference time of 1.264 ms, and validation over process, voltage, and temperature variations. Additionally, a Python-based emulation framework derived from chip measurements is developed and integrated with machine learning techniques, yielding a post-training quantization accuracy of 80.3% over 4-bit resolution on a synthetic retinal dataset modeled after Parasol ON retinal ganglion cells using the CIFAR-10 dataset. These quantitative performance metrics underscore the effectiveness and impact of our approach for potential retinal implant applications.

Unlike traditional methods that implant passive optical components like fibers and rod lenses, optoelectronic semiconductor-based devices directly implant active optoelectronic semiconductors into the brain. This approach offers several advantages—the devices are compact and lightweight, enabling measurement and control without hindering the movement of small animals like mice. Additionally, it allows for simultaneous implantation of multiple devices, and integration with other functions. However, potential temperature increment and biocompatibility due to the active nature of these devices are major drawbacks. This paper reviews novel optoelectronic semiconductor-based devices for measuring and controlling brain nerve function. The advantages of brain-implantable optoelectronic semiconductor devices for fluorescence imaging and photostimulation are highlighted. We address potential limitations and propose future improvements, demonstrating their significant potential to advance neuroscience and pharmacology.

Accurately classifying electroencephalogram (EEG) signals, especially for individuals with neurodegenerative conditions such as myotrophic lateral sclerosis (ALS), poses a significant challenge due to high inter-subject and inter-session changes in signal. This study introduces a novel three-layer graph attention network (GAT) model for motor imagery (MI) classification, utilizing phase locking value (PLV) as the graph input. The GAT model outperforms state-of-the-art deep learning methods, demonstrating notable improvements with a two-class accuracy of 74.06% on an ALS dataset (approximately 320 trials collected over 1-2 months), and 71.89% on the BCI Comp IV 2a Dataset. This improvement demonstrates the effectiveness of graph-based representations to enhance classification performance for neurodegenerative conditions. There are statistically significant reductions in variance compared to state-of-the-art, due to subject-specific attention given by the model during testing. These results support the hypothesis that phase-locking value-based graph representations can enhance neural representations in BCIs, offering promising avenues for more personalized approaches in MI classification. This study highlights the potential for further optimizing GAT architectures and feature sets, pointing to future research directions that could improve performance and efficiency in MI classification tasks whilst establishing a lightweight methodology.

An ultrasound (US) phased array with electronic steering and focusing capability can enable high-resolution, large-scale US interventions in various medical research and clinical experiments. For such applications involving different animal subjects and humans, the phased array system must provide flexibility in generating waveforms with different patterns (including experimental parameters), precise delay resolution between channels, and high voltage across US transducers to produce high US pressure output over extended durations. This paper presents a 16-channel high-voltage phased array system designed for therapeutic medical applications, capable of driving US transducers with pulses up to 100 V and a fine delay resolution of 5 ns, while providing a wide range of sonication waveforms. The modular 16-channel electronics are integrated with a custom-built, 2 MHz, 16-element US transducer array with dimensions of 4.3×11.7×0.7 mm³. In measurements, the phased array system achieved a peak-to-peak US pressure output of up to 6 MPa at a focal depth of 10 mm, with lateral and axial resolution of 0.6 mm and 4.67 mm, respectively. Additionally, the beam focusing and steering capability of the system in measurements and the theoretical analysis of the power consumption of the high-voltage driver (along with measured results) are provided. Finally, the phased array system’s ability to steer and focus the ultrasound beam for blood-brain barrier (BBB) opening in different brain regions is successfully demonstrated in vivo.