AIMC Topic: Action Potentials

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Spiking Neural P Systems with Delay on Synapses.

International journal of neural systems Jul 23, 2020
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapse...
Neural Networks, Computer Animals Models, Neurological Action Potentials Neurons Synapses
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A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation.

Neural computation Jul 20, 2020
Bursting plays an important role in neural communication. At the population level, macroscopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of phase transitions between bur...
Neurons Nerve Net Synaptic Transmission Neural Networks, Computer Computer Simulation Action Potentials Humans
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Time-resolved neurotransmitter detection in mouse brain tissue using an artificial intelligence-nanogap.

Scientific reports Jul 9, 2020
The analysis of neurotransmitters in the brain helps to understand brain functions and diagnose Parkinson's disease. Pharmacological inhibition experiments, electrophysiological measurement of action potentials, and mass analysers have been applied f...
Nanotechnology Cerebral Cortex Dopamine Single Molecule Imaging Brain Mapping Norepinephrine Electrodes Serotonin Electric Conductivity Neurotransmitter Agents Machine Learning Brain Female Animals Mice Action Potentials Mice, Inbred C57BL Parkinson Disease Artificial Intelligence
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Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation.

Circulation. Arrhythmia and electrophysiology Jul 6, 2020
BACKGROUND: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation i...
Neural Networks, Computer Female Reproducibility of Results Aged Humans Signal Processing, Computer-Assisted Registries Atrial Fibrillation Heart Rate Pattern Recognition, Automated Atrial Function, Left Electrophysiologic Techniques, Cardiac Atrial Function, Right Male Time Factors Middle Aged Support Vector Machine Diagnosis, Computer-Assisted Action Potentials Predictive Value of Tests
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Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Circulation. Arrhythmia and electrophysiology Jul 6, 2020
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are...
Prognosis Reproducibility of Results Diagnosis, Computer-Assisted Deep Learning Humans Artificial Intelligence Machine Learning Heart Conduction System Arrhythmias, Cardiac Heart Rate Electrophysiologic Techniques, Cardiac Electrocardiography Action Potentials Predictive Value of Tests Signal Processing, Computer-Assisted
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Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a m...
Diagnosis, Computer-Assisted Treatment Outcome Contrast Media Humans Magnetic Resonance Imaging Machine Learning Meglumine Action Potentials Retrospective Studies Predictive Value of Tests Atrial Fibrillation Aged Recurrence Catheter Ablation Heart Rate Female Male Models, Cardiovascular Proof of Concept Study Reproducibility of Results Middle Aged Patient-Specific Modeling Organometallic Compounds Risk Factors Risk Assessment Pulmonary Veins
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Minimal Spiking Neuron for Solving Multilabel Classification Tasks.

Neural computation May 20, 2020
The multispike tempotron (MST) is a powersul, single spiking neuron model that can solve complex supervised classification tasks. It is also internally complex, computationally expensive to evaluate, and unsuitable for neuromorphic hardware. Here we ...
Neurons Humans Animals Deep Learning Action Potentials
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Memristor networks for real-time neural activity analysis.

Nature communications May 15, 2020
The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and sto...
Electricity Neural Networks, Computer Action Potentials Neurons
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Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.

Scientific reports May 15, 2020
Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the con...
Brain Neurons Humans Action Potentials Synapses Neural Networks, Computer Supervised Machine Learning Pattern Recognition, Automated Algorithms Memory
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Using deep neural networks to detect complex spikes of cerebellar Purkinje cells.

Journal of neurophysiology May 6, 2020
One of the most powerful excitatory synapses in the brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivar...
Action Potentials Macaca mulatta Electrophysiological Phenomena Purkinje Cells Animals Male Deep Learning
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