AIMC Topic: Neural Networks, Computer

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A neuroscience-inspired spiking neural network for EEG-based auditory spatial attention detection.

Neural networks : the official journal of the International Neural Network Society
Recent studies have shown that alpha oscillations (8-13 Hz) enable the decoding of auditory spatial attention. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The prop...

Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an e...

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis.

Advanced materials (Deerfield Beach, Fla.)
Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning ...

Accurate Deep Learning-Aided Density-Free Strategy for Many-Body Dispersion-Corrected Density Functional Theory.

The journal of physical chemistry letters
Using a deep neuronal network (DNN) model trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion (DNN-MBD) model. The DNN strategy bypasses the explicit Hirshfeld partitioning of th...

Multi-Step Hourly Power Consumption Forecasting in a Healthcare Building with Recurrent Neural Networks and Empirical Mode Decomposition.

Sensors (Basel, Switzerland)
Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to...

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.

Sensors (Basel, Switzerland)
Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with t...

Real-time complex light field generation through a multi-core fiber with deep learning.

Scientific reports
The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algor...

Neural sampling machine with stochastic synapse allows brain-like learning and inference.

Nature communications
Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Brain-inspired probabilistic models of neural network can explicitly handle the uncertainty...

Mutual enhancing learning-based automatic segmentation of CT cardiac substructure.

Physics in medicine and biology
Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. Segmenting up to 15 different cardiac substructures can...

A Post-training Quantization Method for the Design of Fixed-Point-Based FPGA/ASIC Hardware Accelerators for LSTM/GRU Algorithms.

Computational intelligence and neuroscience
Recurrent Neural Networks (RNNs) have become important tools for tasks such as speech recognition, text generation, or natural language processing. However, their inference may involve up to billions of operations and their large number of parameters...