AIMC Topic: Neural Networks, Computer

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Early experience with low-pass filtered images facilitates visual category learning in a neural network model.

PloS one
Humans are born with very low contrast sensitivity, meaning that inputs to the infant visual system are both blurry and low contrast. Is this solely a byproduct of maturational processes or is there a functional advantage for beginning life with poor...

Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...

Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction.

Environmental science and pollution research international
Precise wind speed prediction is crucial for the management of the wind power generation systems. However, the stochastic nature of the wind speed makes optimal interval prediction very complicated. In this paper, a hybrid approach consisting of impr...

Resolution estimation in different monolithic PET detectors using neural networks.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We use neural networks to evaluate and compare the spatial resolution of two different simulated monolithic PET detector elements. The effects of mixing events with single photoeffect interactions and multiple Compton scatterings are also st...

Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images.

Sensors (Basel, Switzerland)
Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We pres...

Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.

Scientific reports
Neuroscientific analyses balance between capturing the brain's complexity and expressing that complexity in meaningful and understandable ways. Here we present a novel approach that fully characterizes neural network activity and does so by uniquely ...

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models.

Computational intelligence and neuroscience
The corrosion of steel bars in concrete has a significant impact on the durability of constructed structures. Based on the gray relational analysis (GRA) of the accelerated corrosion data and practical engineering data using MATLAB, a back propagatio...

Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis.

PloS one
Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2...

Dual-Modal Information Bottleneck Network for Seizure Detection.

International journal of neural systems
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimens...

A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation.

IEEE reviews in biomedical engineering
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level cla...