This study presents valuable computational findings on the neural basis of learning new motor memories and the savings using recurrent neural networks. The evidence supporting the claims of the ...

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification ...

This means that the Neural Network occurs repeatedly through time. In a Recurrent Neural Network (RNN), the input can be of any length and output can also be of variable length.

Feng, L., Zhao, C. and Sun, Y. (2021) Dual Attention-Based Encoder-Decoder A Customized Sequence-to-Sequence Learning for Soft Sensor Development. IEEE Transactions on Neural Networks and Learning ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from ...

Abstract P3089: Predicting Reductions In Hemoglobin A1c From Large Dietary Datasets Using Artificial Intelligence Pipeline With Interpretable Encoder-Decoder Deep Neural Networks Martha Tamez, ScD, ...

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking ...

This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder ...