Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) BiSeTe-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.
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http://dx.doi.org/10.1002/adma.202306254 | DOI Listing |
ACS Nano
January 2025
Department of Materials Science and Engineering, University of North Texas, Denton, Texas 76207, United States.
Two-dimensional molybdenum ditelluride (2D MoTe) is an interesting material for artificial synapses due to its unique electronic properties and phase tunability in different polymorphs 2H/1T'. However, the growth of stable and large-scale 2D MoTe on a CMOS-compatible Si/SiO substrate remains challenging because of the high growth temperature and impurity-involved transfer process. We developed a large-scale MoTe film on a Si/SiO wafer by simple sputtering followed by lithium-ion intercalation and applied it to artificial synaptic devices.
View Article and Find Full Text PDFACS Omega
December 2024
School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, U.K.
Modulating memristors optically paves the way for new optoelectronic devices with applications in computer vision, neuromorphic computing, and artificial intelligence. Here, we report on memristors based on a hybrid material of vertically aligned zinc oxide nanorods (ZnO NRs) and poly(methyl methacrylate) (PMMA). The memristors require no forming step and exhibit the typical electronic switching properties of a bipolar memristor.
View Article and Find Full Text PDFSmall
January 2025
School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Homeostasis is essential in biological neural networks, optimizing information processing and experience-dependent learning by maintaining the balance of neuronal activity. However, conventional two-terminal memristors have limitations in implementing homeostatic functions due to the absence of global regulation ability. Here, three-terminal oxide memtransistor-based homeostatic synapses are demonstrated to perform highly linear synaptic weight update and enhanced accuracy in neuromorphic computing.
View Article and Find Full Text PDFACS Nano
January 2025
School of Electrical Engineering, Korea University, Seoul 02841, Korea.
Artificial synapses for neuromorphic computing have been increasingly highlighted, owing to their capacity to emulate brain activity. In particular, solid-state electrolyte-gated electrodes have garnered significant attention because they enable the simultaneous achievement of outstanding synaptic characteristics and mass productivity by adjusting proton migration. However, the inevitable interface traps restrict the protons at the channel-electrolyte interface, resulting in the deterioration of synaptic characteristics.
View Article and Find Full Text PDFAdv Mater
January 2025
School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
The increasing demand for mobile artificial intelligence applications has elevated edge computing to a prominent research area. Silicon materials, renowned for their excellent electrical properties, are extensively utilized in traditional electronic devices. However, the development of silicon materials for flexible neuromorphic computing devices encounters great challenges.
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