AIMC Topic: Neural Networks, Computer

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Relating Human Error-Based Learning to Modern Deep RL Algorithms.

Neural computation
In human error-based learning, the size and direction of a scalar error (i.e., the "directed error") are used to update future actions. Modern deep reinforcement learning (RL) methods perform a similar operation but in terms of scalar rewards. Despit...

A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.

Mathematical biosciences and engineering : MBE
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigatio...

Deep learning insights into distinct patterns of polygenic adaptation across human populations.

Nucleic acids research
Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter t...

[A pilot study on clinical application of three-dimensional morphological completion of lesioned mandibles assisted by generative adversarial networks].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
To explore the clinical application pathway of the CT generative adversarial networks (CTGANs) algorithm in mandibular reconstruction surgery, aiming to provide a valuable reference for this procedure. A clinical exploratory study was conducted, 27...

Toward biologically realistic models of the motor system.

Neuron
In this issue of Neuron, Chiappa et al. describe how neural networks can be trained to perform complex hand motor skills. A key to their approach is curriculum learning, breaking learning into stages, leading to good control.

Crucial rhythms and subnetworks for emotion processing extracted by an interpretable deep learning framework from EEG networks.

Cerebral cortex (New York, N.Y. : 1991)
Electroencephalogram (EEG) brain networks describe the driving and synchronous relationships among multiple brain regions and can be used to identify different emotional states. However, methods for extracting interpretable structural features from b...

CNN-Based Device-Agnostic Feature Extraction From ONH OCT Scans.

Translational vision science & technology
PURPOSE: Optical coherence tomography (OCT)-derived measurements of the optic nerve head (ONH) from different devices are not interchangeable. This poses challenges to patient follow-up and collaborative studies. Here, we present a device-agnostic me...

GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

IEEE journal of biomedical and health informatics
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...

A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Percutaneous puncture procedures, guided by image-guided robotic-assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.

Multi-target neural network model of anxiolytic activity of chemical compounds using correlation convolution of multiple docking energy spectra.

Biomeditsinskaia khimiia
Anxiety disorders are one of the most common mental health pathologies in the world. They require searc h and development of novel effective pharmacologically active substances. Thus, the development of new approaches to the search for anxiolytic sub...