AI Medical Compendium Topic:
Models, Neurological

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Parallel Computing for Brain Simulation.

Current topics in medicinal chemistry
BACKGROUND: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of ...

Stationary Wavelet Transform and AdaBoost with SVM Based Pathological Brain Detection in MRI Scanning.

CNS & neurological disorders drug targets
This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the di...

SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models.

Neuroinformatics
There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a va...

[Thalamocortical Neural Mass Model Simulation and Study Based on Field Programmable Gate Array].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Using the computer to imitate the neural oscillations of the brain is of great significance for the analysis of brain functions.Thalamocortical neural mass model(TNMM)reflects the mechanisms of neural activities by establishing the relationships betw...

Understanding the role of astrocytic GABA in simulated neural networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Astrocytes actively influence the behavior of the surrounding neuronal network including changes of the synaptic plasticity and neuronal excitability. These dynamics are altered in diseases like Alzheimer's, where the release of the gliotransmitter G...

A supervised learning rule for classification of spatiotemporal spike patterns.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically inf...

Tuning electrical stimulation for thalamic visual prosthesis: An autoencoder-based approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes in...

Measuring the agreement between brain connectivity networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association use...

Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

Cerebellum (London, England)
Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular...

Storing Sequences in Binary Tournament-Based Neural Networks.

IEEE transactions on neural networks and learning systems
An extension to a recently introduced architecture of clique-based neural networks is presented. This extension makes it possible to store sequences with high efficiency. To obtain this property, network connections are provided with orientation and ...