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Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders. This letter therefore presents a low-power SoC with both neural connectivity extraction and phase-locked DBS capabilities. A 16-channel low-noise analog front-end (AFE) records local field potentials (LFPs) from multiple brain regions with precise gain matching. A novel low-complexity phase estimator and neural connectivity processor subsequently enable energy-efficient, yet accurate measurement of the instantaneous phase and cross-regional synchrony measures. Through flexible combination of neural biomarkers such as phase synchrony and spectral energy, a four-channel charge-balanced neurostimulator is triggered to treat various pathological brain conditions. Fabricated in 65-nm CMOS, the SoC occupies a silicon area of 2.24 mm and consumes 60 W, achieving over 60% power saving in neural connectivity extraction compared to the state-of-the-art. Extensive measurements demonstrate multi-channel LFP recording, real-time extraction of phase and neural connectivity measures, and phase-locked stimulation in rats.
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http://dx.doi.org/10.1109/lssc.2023.3238797 | DOI Listing |
Soc Cogn Affect Neurosci
September 2025
Faculty of Health and Wellness, City University of Macau, Macau 999078, P.R. China.
Emotional contagion is an important aspect of social interaction. Traditional theories suggest that it relies on mimicry of facial or emotional movements. To address the question of whether there is a distinction between emotional contagion and emotional mimicry, we conducted a meta-analysis using the ALE algorithm to identify brain regions activated by the two tasks.
View Article and Find Full Text PDFChaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
View Article and Find Full Text PDFJ Oral Biol Craniofac Res
August 2025
Neura Integrasi Solusi, Jl. Kebun Raya No. 73, Rejowinangun, Kotagede, Yogyakarta, 55171, Indonesia.
Background: Periodontal disease is an inflammatory condition causing chronic damage to the tooth-supporting connective tissues, leading to tooth loss in adults. Diagnosing periodontitis requires clinical and radiographic examinations, with panoramic radiographs crucial in identifying and assessing its severity and staging. Convolutional Neural Networks (CNNs), a deep learning method for visual data analysis, and Dense Convolutional Networks (DenseNet), which utilize direct feed-forward connections between layers, enable high-performance computer vision tasks with reduced computational demands.
View Article and Find Full Text PDFFront Sports Act Living
August 2025
Faculty of Physical Education, China West Normal University, Nanchong, China.
Understanding how athletes mentally simulate and anticipate actions provides key insights into experience-driven brain plasticity. While previous studies have investigated motor imagery and action anticipation separately, little is known about how their underlying neural mechanisms converge or diverge in expert performers. This study conducted a meta-analysis using activation likelihood estimation (ALE) and meta-analytic connectivity modeling (MACM) to compare brain activation patterns between athletes and non-athletes across both tasks.
View Article and Find Full Text PDFFront Neural Circuits
September 2025
Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan.
Neuronal networks in animal brains are considered to realize specific filter functions through the precise configuration of synaptic weights, which are autonomously regulated without external supervision. In this study, we employ a single Hodgkin-Huxley-type neuron with autapses as a minimum model to computationally investigate how spike-timing-dependent plasticity (STDP) adjusts synaptic weights through recurrent feedback. The results show that the weights undergo oscillatory potentiation or depression with respect to autaptic delay and high-frequency stimulation.
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