AIMC Topic: Action Potentials

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Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness.

IEEE transactions on neural networks and learning systems Feb 28, 2025
Synaptic plasticity plays a critical role in the expression power of brain neural networks. Among diverse plasticity rules, synaptic scaling presents indispensable effects on homeostasis maintenance and synaptic strength regulation. In the current mo...
Action Potentials Neurons Synapses Algorithms Computer Simulation Learning Animals Neuronal Plasticity Models, Neurological Humans Neural Networks, Computer
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Investigation of Inter-Patient, Intra-Patient, and Patient-Specific Based Training in Deep Learning for Classification of Heartbeat Arrhythmia.

Cardiovascular engineering and technology Feb 26, 2025
Effective diagnosis of electrocardiogram (ECG) is one of the simplest and fastest ways to assess the heart's function. In the recent decade, various attempts have been made to automate the classification of electrocardiogram signals to detect heartbe...
Databases, Factual Reproducibility of Results Diagnosis, Computer-Assisted Humans Arrhythmias, Cardiac Predictive Value of Tests Heart Rate Deep Learning Signal Processing, Computer-Assisted Electrocardiography Action Potentials
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Exploring temporal information dynamics in Spiking Neural Networks: Fast Temporal Efficient Training.

Journal of neuroscience methods Feb 25, 2025
BACKGROUND: Spiking Neural Networks (SNNs) hold significant potential in brain simulation and temporal data processing. While recent research has focused on developing neuron models and leveraging temporal dynamics to enhance performance, there is a ...
Action Potentials Neurons Humans Brain Neural Networks, Computer Time Factors Algorithms Models, Neurological
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An accurate and fast learning approach in the biologically spiking neural network.

Scientific reports Feb 24, 2025
Computations adapted from the interactions of neurons in the nervous system have the potential to be a strong foundation for building computers with cognitive functions including decision-making, generalization, and real-time learning. In this contex...
Neurons Nerve Net Pyramidal Cells Learning Humans Models, Neurological Neural Networks, Computer Machine Learning Action Potentials Algorithms
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Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.

The Korean journal of internal medicine Feb 21, 2025
BACKGROUND/AIMS: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent ...
Predictive Value of Tests Atrial Fibrillation Early Diagnosis Deep Learning Electrocardiography Action Potentials Female Humans Aged Neural Networks, Computer Artificial Intelligence Male Middle Aged
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Spiking neural networks on FPGA: A survey of methodologies and recent advancements.

Neural networks : the official journal of the International Neural Network Society Feb 14, 2025
The mimicry of the biological brain's structure in information processing enables spiking neural networks (SNNs) to exhibit significantly reduced power consumption compared to conventional systems. Consequently, these networks have garnered heightene...
Humans Neural Networks, Computer Brain Animals Action Potentials Neurons
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Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

Circulation. Arrhythmia and electrophysiology Feb 10, 2025
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...
Electrophysiologic Techniques, Cardiac Atrial Fibrillation Heart Rate Catheter Ablation Middle Aged Time Factors Electrocardiography Predictive Value of Tests Female Treatment Outcome Registries Humans Aged Action Potentials Male Machine Learning Phenotype
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CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Spiking neural networks (SNNs) mimic their biological counterparts more closely than their predecessors and are considered the third generation of artificial neural networks. It has been proven that networks of spiking neurons have a higher computati...
Humans Neural Networks, Computer Algorithms Neurons Pattern Recognition, Automated Neuronal Plasticity Models, Neurological Action Potentials
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Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust r...
Humans Neural Networks, Computer Algorithms Brain-Computer Interfaces Action Potentials Photic Stimulation Retina Visual Perception
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Reconstruction of Adaptive Leaky Integrate-and-Fire Neuron to Enhance the Spiking Neural Networks Performance by Establishing Complex Dynamics.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Since digital spiking signals can carry rich information and propagate with low computational consumption, spiking neural networks (SNNs) have received great attention from neuroscientists and are regarded as the future development object of neural n...
Neurons Humans Synapses Neural Networks, Computer Algorithms Models, Neurological Computer Simulation Action Potentials Animals
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