AIMC Topic: Neural Networks, Computer

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Evolving neural networks to follow trajectories of arbitrary complexity.

Neural networks : the official journal of the International Neural Network Society
Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic bio...

Uncovering Ecological Patterns with Convolutional Neural Networks.

Trends in ecology & evolution
Using remotely sensed imagery to identify biophysical components across landscapes is an important avenue of investigation for ecologists studying ecosystem dynamics. With high-resolution remotely sensed imagery, algorithmic utilization of image cont...

A RR interval based automated apnea detection approach using residual network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to th...

A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomp...

Number detectors spontaneously emerge in a deep neural network designed for visual object recognition.

Science advances
Humans and animals have a "number sense," an innate capability to intuitively assess the number of visual items in a set, its numerosity. This capability implies that mechanisms to extract numerosity indwell the brain's visual system, which is primar...

All-optical spiking neurosynaptic networks with self-learning capabilities.

Nature
Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computi...

Flux prediction using artificial neural network (ANN) for the upper part of glycolysis.

PloS one
The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexi...

Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning.

IEEE transactions on pattern analysis and machine intelligence
Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution (LR) input. In contrast to the existing patch-wise super-resolution models that divide a face image into re...

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation.

Computers in biology and medicine
BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNe...

A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple s...