A neuroanatomical minimal network model was revisited to elucidate the mechanism of salt concentration memory-dependent chemotaxis observed in Caenorhabditis elegans. C. elegans memorizes the salt ...
Spiking neural networks (SNNs ... is set by an ordinary differential equation shown as follows: For local plasticity, the membrane potential at the firing time is a non-differential spike, so local ...
Gradient disappearance poses a challenge for the deep GCNs designing. DeepGCNs use ResNet, DenseNet, and Void Convolution to train deep neural networks in computer vision ... The specific formula is ...
Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models ...
A technique used to train neural networks that use gradient descent algorithms to update network weights as data moves from the input layer to the hidden layer and the output layer. There are many ...
Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan Booth School of Business, University of Chicago, Chicago, United States Brain Research Center, National Yang Ming ...
When Bruce Talbott began farming his family’s orchards in Palisade on the Western Slope, he used a shovel ... "These families had an incredible network — they knew exactly when the harvests ...
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