AIMC Topic: Brain

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Spiking networks as efficient distributed controllers.

Biological cybernetics
In the brain, networks of neurons produce activity that is decoded into perceptions and actions. How the dynamics of neural networks support this decoding is a major scientific question. That is, while we understand the basic mechanisms by which neur...

A self-organizing short-term dynamical memory network.

Neural networks : the official journal of the International Neural Network Society
Working memory requires information about external stimuli to be represented in the brain even after those stimuli go away. This information is encoded in the activities of neurons, and neural activities change over timescales of tens of milliseconds...

Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.

IEEE transactions on cybernetics
Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The changes caused by ASD are associated with different age- and sex-related patterns in neuroimaging data. Ho...

Quantitative susceptibility mapping using deep neural network: QSMnet.

NeuroImage
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantita...

Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.

Computers in biology and medicine
In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which ca...

Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.

Physics in medicine and biology
Positron emission tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid sys...

Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

Scientific reports
Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveal...

Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.

Journal of biological physics
In earlier work, we laid out the foundation for explaining the quantum-like behavior of neural systems in the basic kinematic case of clusters of neuron-like units. Here we extend this approach to networks and begin developing a dynamical theory for ...

Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach.

Neuroscience
A large number of studies have demonstrated costly punishment to unfair events across human societies. However, individuals exhibit a large heterogeneity in costly punishment decisions, whereas the neuropsychological substrates underlying the heterog...

MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection.

IEEE journal of biomedical and health informatics
Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image super-resolution (SR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. Over the past several years, th...