Results 221 to 230 of about 108,821 (296)

Material Strategies for Stimulation and Recording in Neural Biocomputing Platforms

open access: yesAdvanced Electronic Materials, EarlyView.
Material strategies enabling stimulation and recording are central to neural biocomputing systems. This review examines how electronic materials govern the encoding of inputs and decoding of outputs in living neural networks. Advances in electrical, optical, and multimodal interfaces highlight emerging design principles for biocomputing platforms ...
Sehong Kang   +5 more
wiley   +1 more source

Non-invasive brain stimulation to modulate neural activity in Parkinson's disease. [PDF]

open access: yesNPJ Parkinsons Dis
Bange M   +4 more
europepmc   +1 more source

Cavity Microelectrode Arrays for Electrical Recordings From Neurons

open access: yesAdvanced Electronic Materials, EarlyView.
Microelectrode arrays (MEAs) are used to study electrophysiological activity. However, their signals are small with high noise. By adding a 100‐nanometer‐high cavity above the electrode, which reduces impedance without affecting resolution, we improve signal quality.
Johannes Lewen   +2 more
wiley   +1 more source

MECHANISMS OF NON-INVASIVE BRAIN STIMULATION

open access: yesFrontiers in Human Neuroscience, 2014
openaire   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

open access: yesAdvanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu   +4 more
wiley   +1 more source

Non-invasive brain stimulation and rehabilitation

open access: yesAnnals of Physical and Rehabilitation Medicine, 2014
openaire   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

open access: yesAdvanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas   +4 more
wiley   +1 more source

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