AIMC Topic: Neural Networks, Computer

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TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.

Journal of chemical information and modeling
Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is ...

Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron.

International journal of neural systems
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carr...

Genetic algorithm search for the worst-case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks.

Magnetic resonance in medicine
PURPOSE: This paper presents a method to search for the worst-case configuration leading to the highest RF exposure for a multiconfiguration implantable fixation system under MRI.

Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

International orthopaedics
BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported re...

Using Artificial Intelligence for Pattern Recognition in a Sports Context.

Sensors (Basel, Switzerland)
Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding...

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals.

Sensors (Basel, Switzerland)
Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should ...

Assessment of Generative Adversarial Networks Model for Synthetic Optical Coherence Tomography Images of Retinal Disorders.

Translational vision science & technology
PURPOSE: To assess whether a generative adversarial network (GAN) could synthesize realistic optical coherence tomography (OCT) images that satisfactorily serve as the educational images for retinal specialists, and the training datasets for the clas...

Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Aged people usually are more to be diagnosed with retinal diseases in developed countries. Retinal capillaries leakage into the retina swells and causes an acute vision loss, which is called age-related macular degeneration...

Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar neural network for scalable on-chip learning.

Nanotechnology
On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully connected neural network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or...

Error bounds for deep ReLU networks using the Kolmogorov-Arnold superposition theorem.

Neural networks : the official journal of the International Neural Network Society
We prove a theorem concerning the approximation of multivariate functions by deep ReLU networks, for which the curse of the dimensionality is lessened. Our theorem is based on a constructive proof of the Kolmogorov-Arnold superposition theorem, and o...