Results 51 to 60 of about 95,522 (318)
, 2018 Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state ...
Indiveri, Giacomo +3 more
core +1 more source
Accelerated physical emulation of Bayesian inference in spiking neural networks [PDF]
, 2019 The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary ...
Baumbach, Andreas +23 more
core +2 more sources
Transistor-Based Synaptic Devices for Neuromorphic Computing
CrystalsCurrently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic ...
Wen Huang +4 more
doaj +1 more source
Memory and Synaptic Devices Based on Emerging 2D Ferroelectricity
Advanced Electronic Materials, 2023 Memory devices are an essential part of modern electronics. Efforts to move beyond the traditional “read” and “write” of digital information in volatile and non‐volatile memory devices are leading to the rapid growth of neuromorphic technology.
Yanggeun Joo +4 more
doaj +1 more source
Nanoscale-targeted patch-clamp recordings of functional presynaptic ion channels [PDF]
, 2013 Important modulatory roles have been attributed to presynaptic NMDA receptors (NMDARs) located on cerebellar interneuron terminals. Evidence supporting a presynaptic location includes an increase in the frequency of mini events following the application ...
Benton, DCH +4 more
core +1 more source
Synaptic metaplasticity with multi-level memristive devices
2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2023 Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when learning a new one.
D’Agostino, S +7 more
openaire +3 more sources
Ultralow Power Wearable Heterosynapse with Photoelectric Synergistic Modulation
Advanced Science, 2020 Although the energy consumption of reported neuromorphic computing devices inspired by biological systems has become lower than traditional memory, it still remains greater than bio‐synapses (≈10 fJ per spike). Herein, a flexible MoS2‐based heterosynapse
Tian‐Yu Wang +8 more
doaj +1 more source
Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Myasthenia gravis (MG) is a rare, autoimmune disorder characterized by fluctuating muscle weakness and potential life‐threatening crises. While continuous specialized care is essential, access barriers often delay timely interventions. To address this, we developed MyaLink, a telemedical platform for MG patients.
Maike Stein +13 more
wiley +1 more source
Neuromorphic Computing and EngineeringNeuromorphic devices that emulate biological neural systems have been actively studied to overcome the limitations of conventional von Neumann computing structure.
Ui-Chan Jeong +3 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Retrograde trans‐synaptic degeneration (rTSD) from posterior visual pathway lesions in multiple sclerosis (MS) is characterized by hemi‐macular ganglion cell‐inner plexiform layer (GCIPL) thinning and contralateral visual field loss.
Abdul Jaber Tayem +17 more
wiley +1 more source