AIMC Topic: Neurons

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Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning.

NeuroImage Oct 1, 2021
Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy ...
Adult Male Female Young Adult Neurons Individuality Visual Cortex Deep Learning Magnetic Resonance Imaging Humans Neural Networks, Computer
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Quantitative neuronal morphometry by supervised and unsupervised learning.

STAR protocols Sep 30, 2021
We present a protocol to characterize the morphological properties of individual neurons reconstructed from microscopic imaging. We first describe a simple procedure to extract relevant morphological features from digital tracings of neural arbors. T...
Software Animals Machine Learning Image Processing, Computer-Assisted Zebrafish Neurons Microscopy
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Determining Top Fully Connected Layer's Hidden Neuron Count for Transfer Learning, Using Knowledge Distillation: a Case Study on Chest X-Ray Classification of Pneumonia and COVID-19.

Journal of digital imaging Sep 29, 2021
Deep convolutional neural network (CNN)-assisted classification of images is one of the most discussed topics in recent years. Continuously innovation of neural network architectures is making it more correct and efficient every day. But training a n...
Humans COVID-19 SARS-CoV-2 Neurons Deep Learning X-Rays Pneumonia
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Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Cells Sep 25, 2021
Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progressi...
Nerve Degeneration Deep Learning Neurodegenerative Diseases Disease Models, Animal Cell Death Neurons Humans Axons Animals
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Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks.

Sensors (Basel, Switzerland) Sep 25, 2021
Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connectio...
Internet of Things Neurons Motion Neural Networks, Computer Algorithms
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Visual explanations from spiking neural networks using inter-spike intervals.

Scientific reports Sep 24, 2021
By emulating biological features in brain, Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional deep learning. To make SNNs ubiquitous, a 'visual explanation' technique for analysing and explaining the internal spike b...
Neurons Visual Perception Models, Neurological Action Potentials Humans Neural Networks, Computer Machine Learning Brain
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Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors.

ACS nano Sep 20, 2021
The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need fo...
Artificial Intelligence Neurons Synapses Humans Neural Networks, Computer Visual Perception
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A Scalable Artificial Neuron Based on Ultrathin Two-Dimensional Titanium Oxide.

ACS nano Sep 17, 2021
A spiking neural network consists of artificial synapses and neurons and may realize human-level intelligence. Unlike the widely reported artificial synapses, the fabrication of large-scale artificial neurons with good performance is still challengin...
Titanium Neurons Neural Networks, Computer Models, Neurological Humans
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Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning.

Sensors (Basel, Switzerland) Sep 14, 2021
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prio...
Neural Networks, Computer Machine Learning Neurons Recognition, Psychology Data Analysis
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Subgroup Preference Neural Network.

Sensors (Basel, Switzerland) Sep 12, 2021
Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network () that combines multiple networks have different activatio...
Cluster Analysis Neural Networks, Computer Neurons Learning
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