AIMC Topic: Brain

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Bio-inspired, task-free continual learning through activity regularization.

Biological cybernetics
The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approach...

Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim.

International journal of neural systems
Spiking Neural Networks (SNNs) help achieve brain-like efficiency and functionality by building neurons and synapses that mimic the human brain's transmission of electrical signals. However, optimal SNN implementation requires a precise balance of pa...

SWsnn: A Novel Simulator for Spiking Neural Networks.

Journal of computational biology : a journal of computational molecular cell biology
Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other f...

Deep learning-based reconstruction for canine brain magnetic resonance imaging could improve image quality while reducing scan time.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Optimal magnetic resonance imaging (MRI) quality and shorter scan time are challenging to achieve in veterinary practices. Recently, deep learning-based reconstruction (DLR) has been proposed for ideal image quality. We hypothesized that DLR-based MR...

Subtle adversarial image manipulations influence both human and machine perception.

Nature communications
Although artificial neural networks (ANNs) were inspired by the brain, ANNs exhibit a brittleness not generally observed in human perception. One shortcoming of ANNs is their susceptibility to adversarial perturbations-subtle modulations of natural i...

Automatic Deep Learning-Based Pipeline for Automatic Delineation and Measurement of Fetal Brain Structures in Routine Mid-Trimester Ultrasound Images.

Fetal diagnosis and therapy
INTRODUCTION: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images.

Deep Learning Segmentation of the Nucleus Basalis of Meynert on 3T MRI.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability amo...

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology.

Neuroscience letters
Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of featur...

Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in and studies.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in or studies. The purpos...

Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination.

Tomography (Ann Arbor, Mich.)
In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomogr...