AIMC Topic: Nerve Net
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State-of-the-art methods in healthcare text classification system: AI paradigm.
Frontiers in bioscience (Landmark edition)
Jan 1, 2020
Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual infor...
A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models.
Journal of neurophysiology
Dec 18, 2019
We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fir...
Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity.
Journal of neural engineering
Dec 16, 2019
OBJECTIVE: Somatic symptom disorder (SSD) is a reflection of medically unexplained physical symptoms that lead to distress and impairment in social and occupational functioning. SSD is phenomenologically diagnosed and its neurobiology remains unsolve...
From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior.
Neural computation
Dec 13, 2019
The study of neuronal interactions is at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, and the Brainome) that attempt to obtain a detailed map of the entire brain. Under...
Behavioural analysis of single-cell aneural ciliate, using machine learning approaches.
Journal of the Royal Society, Interface
Dec 4, 2019
There is still a significant gap between our understanding of neural circuits and the behaviours they compute-i.e. the computations performed by these neural networks (Carandini 2012 , 507-509. (doi:10.1038/nn.3043)). Cellular decision-making process...
Disentangling brain functional network remodeling in corticobasal syndrome - A multimodal MRI study.
NeuroImage. Clinical
Dec 2, 2019
OBJECTIVE: The clinical diagnosis of corticobasal syndrome (CBS) represents a challenge for physicians and reliable diagnostic imaging biomarkers would support the diagnostic work-up. We aimed to investigate the neural signatures of CBS using multimo...
Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.
Computational and mathematical methods in medicine
Nov 4, 2019
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect...
Brain Mapping
Young Adult
Medical Informatics
Adult
Support Vector Machine
Pattern Recognition, Automated
Algorithms
Male
Magnetic Resonance Imaging
Nerve Net
Brain
Humans
Cluster Analysis
Female
Neural Networks, Computer
Reproducibility of Results
Middle Aged
Machine Learning
Depressive Disorder, Major
Diagnosis, Computer-Assisted
Adolescent
Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer's disease continuum.
Journal of neural engineering
Oct 29, 2019
OBJECTIVE: The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due t...
Regression-based machine-learning approaches to predict task activation using resting-state fMRI.
Human brain mapping
Oct 22, 2019
Resting-state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting-state network features to activation z-scores. The question remains whether the relatively simplistic GLM ...
Test-retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network.
Journal of neuroscience methods
Oct 15, 2019
BACKGROUND: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, t...
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