AI Medical Compendium Journal:
Neuroinformatics

Showing 51 to 60 of 85 articles

NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods.

Neuroinformatics
NengoDL is a software framework designed to combine the strengths of neuromorphic modelling and deep learning. NengoDL allows users to construct biologically detailed neural models, intermix those models with deep learning elements (such as convoluti...

Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging.

Neuroinformatics
An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable i...

Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning.

Neuroinformatics
Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, o...

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites.

Neuroinformatics
Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the...

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.

Neuroinformatics
Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotroph...

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience.

Neuroinformatics
The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through ...

An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging.

Neuroinformatics
Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. Detailed studies of the microglial response after TBI require high throughput quantification of changes in microglial count and morphology in histological se...

Automated Metadata Suggestion During Repository Submission.

Neuroinformatics
Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these...

Cocaine-Induced Preference Conditioning: a Machine Vision Perspective.

Neuroinformatics
Existing work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-dri...

The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease.

Neuroinformatics
A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be dire...