AIMC Topic: Neural Networks, Computer

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Memristive device based learning for navigation in robots.

Bioinspiration & biomimetics
Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data,...

High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

Computational intelligence and neuroscience
Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to vid...

A universal deep learning approach for modeling the flow of patients under different severities.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, whic...

Margined winner-take-all: New learning rule for pattern recognition.

Neural networks : the official journal of the International Neural Network Society
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer,...

Working Memory and Decision-Making in a Frontoparietal Circuit Model.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and dis...

Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.

Computational intelligence and neuroscience
From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities. Focusing on...

Deep Recurrent Neural Networks for Human Activity Recognition.

Sensors (Basel, Switzerland)
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples ...

Matrix completion by deep matrix factorization.

Neural networks : the official journal of the International Neural Network Society
Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists consi...

Efficient k-NN Implementation for Real-Time Detection of Cough Events in Smartphones.

IEEE journal of biomedical and health informatics
The potential  of telemedicine in respiratory health care has not been completely unveiled in part due to the inexistence of reliable objective measurements of symptoms such as cough. Currently available cough detectors are uncomfortable and expensiv...

Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.

Radiology
Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study....