AIMC Topic: Brain

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A bi-functional three-terminal memristor applicable as an artificial synapse and neuron.

Nanoscale
Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synapti...

Bridging Neuroscience and Robotics: Spiking Neural Networks in Action.

Sensors (Basel, Switzerland)
Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in dynamically changing environments. To advance this, developments have turned towards understanding t...

Reducing Gadolinium Contrast With Artificial Intelligence.

Journal of magnetic resonance imaging : JMRI
Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast...

The Neuroscience of Human and Artificial Intelligence Presence.

Annual review of psychology
Two decades of social neuroscience and neuroeconomics research illustrate the brain mechanisms that are engaged when people consider human beings, often in comparison to considering artificial intelligence (AI) as a nonhuman control. AI as an experim...

Decoding fMRI data with support vector machines and deep neural networks.

Journal of neuroscience methods
BACKGROUND: Multivoxel pattern analysis (MVPA) examines fMRI activation patterns associated with different cognitive conditions. Support vector machines (SVMs) are the predominant method in MVPA. While SVM is intuitive and easy to apply, it is mainly...

"A net for everyone": fully personalized and unsupervised neural networks trained with longitudinal data from a single patient.

BMC medical imaging
BACKGROUND: With the rise in importance of personalized medicine and deep learning, we combine the two to create personalized neural networks. The aim of the study is to show a proof of concept that data from just one patient can be used to train dee...

Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data.

Computer methods and programs in biomedicine
BACKGROUND: White matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted wi...

Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.

Scientific reports
In this study, Neural Networks (NN) modelling has emerged as a promising tool for predicting outcomes in patients with Brain Stroke (BS) by identifying key risk factors. In this longitudinal study, we enrolled 332 patients form Imam hospital in Ardab...

A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.

Journal of magnetic resonance imaging : JMRI
Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widel...