The ultrastructural details of presynapses formed between artificial substrates of submicrometer silica beads and hippocampal neurons are visualized via cryo-electron microscopy (cryo-EM). The silica beads are derivatized by poly-d-lysine or lipid bilayers. Molecular features known to exist at presynapses are clearly present at these artificial synapses, as visualized by cryo-EM. Key synaptic features such as the membrane contact area at synaptic junctions, the presynaptic bouton containing presynaptic vesicles, as well as microtubular structures can be identified. This is the first report of the direct, label-free observation of ultrastructural details of artificial synapses.
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http://dx.doi.org/10.1021/cn200094j | DOI Listing |
Trends Neurosci
January 2025
Hefei National Laboratory for Physical Sciences at the Microscale, Department of Neurology in the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China. Electronic address:
The precise organization of the complex set of synaptic proteins at the nanometer scale is crucial for synaptic transmission. At the heart of this nanoscale architecture lies the nanocolumn. This aligns presynaptic neurotransmitter release with a high local density of postsynaptic receptor channels, thereby optimizing synaptic strength.
View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.
Research on memristive devices to seamlessly integrate and replicate the dynamic behaviors of biological synapses will illuminate the mechanisms underlying parallel processing and information storage in the human brain, thereby affording novel insights for the advancement of artificial intelligence. Here, an artificial electric synapse is demonstrated on a one-step Mo-selenized MoSe memristor, having not only long-term stable resistive switching characteristics (reset 0.51 ± 0.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Department of Physics, Indian Institute of Technology Guwahati, Guwahati 781039, India.
The discovery of moiré physics in two-dimensional (2D) materials has opened new avenues for exploring unique physical and chemical properties induced by intralayer/interlayer interactions. This study reports the experimental observation of moiré patterns in 2D bismuth oxyselenide (BiOSe) nanosheets grown through one-pot chemical reaction methods and a sonication-assisted layer separations technique. Our findings demonstrate that these moiré patterns result from the angular stacking of the nanosheets at various twist angles, leading to the formation of moiré superlattices (MSLs) with distinct periodicities.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Physics, Beihang University, Beijing 100191, China.
Exploiting biomimetic perception of invisible spectra in flexible artificial human vision systems (HVSs) is crucial for real-time dynamic information processing. Nevertheless, the fast processing of motion objects in natural environments poses a challenge, necessitating that these artificial HVSs simultaneously have swift photoresponse and nonvolatile memory. Here, inspired by the human retina, we propose a flexible UV neuromorphic visual synaptic device (NeuVSD) based on GaO@GaN-composited nanowires for dynamic visual perception.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Department of Physics, College of Science, Qiqihar University, Qiqihar 161006, China.
In the era of artificial intelligence, there has been a rise in novel computing methods due to the increased demand for rapid and effective data processing. It is of great significance to develop memristor devices capable of emulating the computational neural network of the brain, especially in the realm of artificial intelligence applications. In this work, a memristor based on NiAl-layered double hydroxides is presented with excellent electrical performance, including analog resistive conversion characteristics and the effect of multi-level conductivity modulation.
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