AIMC Topic: Neural Networks, Computer

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Rhythmic modulation of thalamic oscillations depends on intrinsic cellular dynamics.

Journal of neural engineering
OBJECTIVE: Rhythmic brain stimulation has emerged as a powerful tool to modulate cognition and to target pathological oscillations related to neurological and psychiatric disorders. However, we lack a systematic understanding of how periodic stimulat...

Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Evaluation of endoscopic disease activity for patients with ulcerative colitis (UC) is important when determining the treatment of choice. However, endoscopists require a certain period of training to evaluate the activity of inf...

A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties.

Environmental science and pollution research international
Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA)...

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.

Analytical chemistry
Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assi...

Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks.

Scientific reports
Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4...

A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.

Physiological measurement
OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our...

Recognizing Brain States Using Deep Sparse Recurrent Neural Network.

IEEE transactions on medical imaging
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rare...

Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging.

NeuroImage
An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNN) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI...

Evaluating performance of neural codes in model neural communication networks.

Neural networks : the official journal of the International Neural Network Society
Information needs to be appropriately encoded to be reliably transmitted over physical media. Similarly, neurons have their own codes to convey information in the brain. Even though it is well-known that neurons exchange information using a pool of s...

Classification of acute lymphoblastic leukemia using deep learning.

Microscopy research and technique
Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose A...