AIMC Topic: Brain

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Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting.

IEEE transactions on neural networks and learning systems
It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural ...

Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. Howev...

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain a...

Insomnia disorder diagnosed by resting-state fMRI-based SVM classifier.

Sleep medicine
BACKGROUND: The main classification systems of sleep disorders are based on the subjective self-reported criteria. Objective measures are essential to characterize the nocturnal sleep disturbance, identify daytime impairment, and determine the course...

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template.

NeuroImage
Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established al...

Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal.

Computers in biology and medicine
Detection of mental disorders such as schizophrenia (SZ) through investigating brain activities recorded via Electroencephalogram (EEG) signals is a promising field in neuroscience. This study presents a hybrid brain effective connectivity and deep l...

Stress Classification Using Brain Signals Based on LSTM Network.

Computational intelligence and neuroscience
The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality...

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation.

Computers in biology and medicine
Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep le...

Predicting brain structural network using functional connectivity.

Medical image analysis
Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and fun...

Deep neural networks learn general and clinically relevant representations of the ageing brain.

NeuroImage
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in ...