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Action Potentials

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GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.

Pharmaceutical research 40140128
PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syn...
Action Potentials Algorithms Cardiotoxicity Computer Simulation Deep Learning ERG1 Potassium Channel Ether-A-Go-Go Potassium Channels Graph Neural Networks Humans Machine Learning Neural Networks, Computer Potassium Channel Blockers
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Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks.

Nature neuroscience 40128390
The temporal order of a sequence of events has been thought to be reflected in the ordered firing of neurons at different phases of theta oscillations. Here we assess this by measuring single neuron activity (1,420 neurons) and local field potentials...
Action Potentials Adult Epilepsy Female Humans Male Memory, Short-Term Nerve Net Neural Networks, Computer Neurons Recurrent Neural Networks Temporal Lobe Theta Rhythm Young Adult
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Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data.

Scientific reports 40301534
Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor da...
Action Potentials Animals Bicuculline Biosensing Techniques Machine Learning Microelectrodes Nerve Net Neurons Rats
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Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.

BMC cardiovascular disorders 40295939
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...
Action Potentials Arrhythmias, Cardiac Body Surface Potential Mapping Computer Simulation Deep Learning Electrocardiography Electrophysiologic Techniques, Cardiac Heart Rate Humans Models, Cardiovascular Neural Networks, Computer Predictive Value of Tests Reproducibility of Results
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Heterogeneous quantization regularizes spiking neural network activity.

Scientific reports 40268966
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a ...
Action Potentials Animals Humans Models, Neurological Nerve Net Neural Networks, Computer Neurons
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Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.

Sensors (Basel, Switzerland) 40218789
Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition acc...
Action Potentials Algorithms Convolutional Neural Networks Deep Learning Fingers Humans Neural Networks, Computer Neurons Support Vector Machine Veins
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

BMC cardiovascular disorders 40189503
A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an increasing mortality rate in recent years. Regular monitoring is essential for effective management, as early detection and timely treatment greatly imp...
Action Potentials Algorithms Arrhythmias, Cardiac Diagnosis, Computer-Assisted Electrocardiography Heart Rate Humans Machine Learning Predictive Value of Tests Reproducibility of Results Signal Processing, Computer-Assisted
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Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation.

Neural computation 40262747
Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robustly, but it...
Action Potentials Adaptation, Physiological Animals Brain Humans Models, Neurological Neural Networks, Computer Neurons
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Neural Code Translation With LIF Neuron Microcircuits.

Neural computation 40262750
Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each encoding me...
Action Potentials Animals Humans Models, Neurological Neural Networks, Computer Neurons
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Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks.

Neural computation 40262748
The study of brain activity spans diverse scales and levels of description and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks p...
Action Potentials Animals Brain Computer Simulation Humans Models, Neurological Nerve Net Neural Networks, Computer Neurons
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