Neural networks : the official journal of the International Neural Network Society
Jun 14, 2018
This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by...
In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which ca...
The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both lea...
Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, ...
First-order tactile neurons have spatially complex receptive fields. Here we use machine-learning tools to show that such complexity arises for a wide range of training sets and network architectures. Moreover, we demonstrate that this complexity ben...
Neural networks : the official journal of the International Neural Network Society
Jun 13, 2018
By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms. We prove that the proposed two-hidd...
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. I...
PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (r...
BACKGROUND: As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorit...
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