AIMC Topic: Brain

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HybridSNN: Combining Bio-Machine Strengths by Boosting Adaptive Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as c...

Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality.

Tomography (Ann Arbor, Mich.)
This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminar...

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.

PloS one
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing ta...

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Physics in medicine and biology
Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI...

I know what you're thinking; can neuroimaging truly reveal our innermost thoughts?

BioTechniques
[Formula: see text] Advances in neuroimaging, combined with developments in artificial intelligence software, have allowed researchers to noninvasively decode the brain and 'read the mind'.

A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost.

Science advances
Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-...

Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study.

Depression and anxiety
Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showe...

A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remain...

Consciousness: a neurosurgical perspective.

Acta neurochirurgica
Neurosurgeons are in a unique position to shed light on the neural basis for consciousness, not only by their clinical care of patients with compromised states of consciousness, but also by employing neurostimulation and neuronal recordings through i...