Results 111 to 120 of about 7,083,147 (335)
Temporal-Kernel Recurrent Neural Networks [PDF]
Neural Networks, 2010 A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using ...
Ilya Sutskever, Geoffrey E. Hinton
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Adenosine triphosphate as a modulator of protein interactions and stability
FEBS Open Bio, EarlyView.ATP is best known as the cell's energy currency, but it also shapes how proteins fold, interact, aggregate and form biomolecular condensates. This review explains the emerging physical principles behind these effects, including weak binding to charged protein regions, magnesium‐dependent behaviour and concentration‐dependent control of protein ...
Shuyuan Tan, Robin Curtis
wiley +1 more source
Chronobiology of Cancer: How Aging Fuels Oncogenesis at the Molecular Level
Aging and Cancer, EarlyView.This graphical abstract illustrates the key biological pathways linking aging with cancer development and progression. In the upper left, cumulative exposure to ultraviolet radiation, toxins, and reactive oxygen species (ROS) causes DNA damage and genomic instability, whereas age‐related decline in repair mechanisms, such as ATM/ATR, BER, and NER ...
Anu Singh, Aroonima Misra, Sufian Zaheer
wiley +1 more source
EMP response modeling of TVS based on the recurrent neural network
Journal of Hebei University of Science and Technology, 2015 Due to the larger workload in the implementation process and the poor consistence between the test results and actual situation problems when using the transmission line pulse (TLP) testing methods, a modeling method based on the recurrent neural network
Zhiqiang JI +3 more
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Memristive recurrent neural network
Neurocomputing, 2018 Abstract It is reported a continuous-time neural network in CMOS that uses memristors. These nanodevices are used to achieve some analog functions such as constant current sourcing, decaying term emulation, and resistive connection; all of them representing parameters of the neural network.
Gerardo Marcos Tornez-Xavier +3 more
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Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi +8 more
wiley +1 more source
Reversible Recurrent Neural Networks
CoRR, 2018 Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained. Reversible RNNs---RNNs for which the hidden-to-hidden transition can be reversed---offer a path to reduce the memory requirements of training, as hidden ...
Matthew MacKay +3 more
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Recurrent Neural Networks as Electrical Networks, a Formalization
, 2023 Since the 1980s, and particularly with the Hopfield model, recurrent neural networks or RNN became a topic of great interest. The first works of neural networks consisted of simple systems of a few neurons that were commonly simulated through analogue electronic circuits.
Mariano Caruso, Cecilia Jarne
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RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu +21 more
wiley +1 more source
MathematicsThis paper presents a varying-parameter finite-time recurrent neural network, called a varying-factor finite-time recurrent neural network (VFFTRNN), which is able to solve the solution of the time-varying Sylvester equation online.
Haoming Tan +6 more
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