AIMC Topic: Brain

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Lateralized Learning to Solve Complex Boolean Problems.

IEEE transactions on cybernetics
Modern classifier systems can effectively classify targets that consist of simple patterns. However, they can fail to detect hierarchical patterns of features that exist in many real-world problems, such as understanding speech or recognizing object ...

Model metamers reveal divergent invariances between biological and artificial neural networks.

Nature neuroscience
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model ...

Detection and classification of adult epilepsy using hybrid deep learning approach.

Scientific reports
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately p...

Exploring the performance of implicit neural representations for brain image registration.

Scientific reports
Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of bra...

Unsupervised deep learning registration model for multimodal brain images.

Journal of applied clinical medical physics
Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state-of-...

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

PloS one
Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets....

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

Nature communications
Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the mult...

Multimodal hybrid convolutional neural network based brain tumor grade classification.

BMC bioinformatics
An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causin...