AI Medical Compendium Topic

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Models, Neurological

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Improved object recognition using neural networks trained to mimic the brain's statistical properties.

Neural networks : the official journal of the International Neural Network Society
The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many tasks. As they are trained for...

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning.

International journal of molecular sciences
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with e...

Spiking Neural P Systems with Delay on Synapses.

International journal of neural systems
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapse...

Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

Computational and mathematical methods in medicine
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feat...

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Computational and mathematical methods in medicine
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In t...

Deep Reinforcement Learning and Its Neuroscientific Implications.

Neuron
The emergence of powerful artificial intelligence (AI) is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning in tasks such as image classification. Ho...

Neural networks of different species, brain areas and states can be characterized by the probability polling state.

The European journal of neuroscience
Cortical networks are complex systems of a great many interconnected neurons that operate from collective dynamical states. To understand how cortical neural networks function, it is important to identify their common dynamical operating states from ...

Brain-optimized extraction of complex sound features that drive continuous auditory perception.

PLoS computational biology
Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match ...

NeuroConstruct-based implementation of structured-light stimulated retinal circuitry.

BMC neuroscience
BACKGROUND: Retinal circuitry provides a fundamental window to neural networks, featuring widely investigated visual phenomena ranging from direction selectivity to fast detection of approaching motion. As the divide between experimental and theoreti...

A Model for Structured Information Representation in Neural Networks of the Brain.

eNeuro
Humans can reason at an abstract level and structure information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the br...