Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in patte...
Neural networks : the official journal of the International Neural Network Society
Oct 16, 2019
Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-s...
Neural networks : the official journal of the International Neural Network Society
Oct 16, 2019
Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain signal classifiers based on SNNs. The work shows the capabilities of this type of Spiking Neurons in ...
Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. RNNs are trained to reproduce animal behavior while also capturing key statistics of empirically recorded neural ac...
Journal of chemical information and modeling
Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
OBJECTIVE: Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of ...
BACKGROUND: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, t...
A recent paper (Chang & Tsao, 2017) reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition ...
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking...
Temporal relations are crucial in constructing a timeline over the course of clinical care, which can help medical practitioners and researchers track the progression of diseases, treatments and adverse reactions over time. Due to the rapid adoption ...
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