AIMC Topic: Action Potentials

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Small universal spiking neural P systems with dendritic/axonal delays and dendritic trunk/feedback.

Neural networks : the official journal of the International Neural Network Society Feb 16, 2021
In spiking neural P (SN P) systems, neurons are interconnected by means of synapses, and they use spikes to communicate with each other. However, in biology, the complex structure of dendritic tree is also an important part in the communication schem...
Action Potentials Neural Networks, Computer Reaction Time Feedback Axons Synapses Models, Neurological Humans Dendrites
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Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.

Circulation. Arrhythmia and electrophysiology Feb 12, 2021
[Figure: see text].
Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Atrial Fibrillation Risk Assessment Treatment Outcome Action Potentials Recurrence Atrial Function, Left Fractals Pulmonary Veins Heart Atria Heart Rate Catheter Ablation Machine Learning Retrospective Studies Tomography, X-Ray Computed Male Middle Aged Female Risk Factors Humans
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Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

PLoS computational biology Feb 10, 2021
The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented thro...
Excitatory Postsynaptic Potentials Cerebellar Cortex Synaptic Transmission Models, Neurological Animals Interneurons Neural Pathways Humans Action Potentials Signal Transduction Purkinje Cells Cerebellum Normal Distribution Neurons Neural Networks, Computer Neuronal Plasticity Synapses Computer Simulation
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A new recursive least squares-based learning algorithm for spiking neurons.

Neural networks : the official journal of the International Neural Network Society Feb 7, 2021
Spiking neural networks (SNNs) are regarded as effective models for processing spatio-temporal information. However, their inherent complexity of temporal coding makes it an arduous task to put forward an effective supervised learning algorithm, whic...
Least-Squares Analysis Neurons Humans Spatio-Temporal Analysis Machine Learning Models, Neurological Action Potentials
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SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm.

Journal of neural engineering Feb 5, 2021
Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more ne...
Humans Action Potentials Signal Processing, Computer-Assisted Neurons Electrodes, Implanted Algorithms Animals Deep Learning
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The role of individual neuron ion conductances in the synchronization processes of neuron networks.

Neural networks : the official journal of the International Neural Network Society Jan 29, 2021
The partial phase synchronization (sometimes called cooperation) of neurons is fundamental for the understanding of the complex behavior of the brain. The lack or the excess of synchronization can generate brain disorders like Parkinson's disease and...
Action Potentials Humans Neurons Neural Networks, Computer Cortical Synchronization Models, Neurological
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Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

American journal of physiology. Heart and circulatory physiology Jan 29, 2021
Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demand...
Atrial Fibrillation Heart Rate Pattern Recognition, Automated Humans Therapy, Computer-Assisted Action Potentials Machine Learning Neural Networks, Computer Heart Function Tests Predictive Value of Tests Clinical Decision-Making Artificial Intelligence Heart Conduction System Diagnosis, Computer-Assisted
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Deep Learning for Robust Decomposition of High-Density Surface EMG Signals.

IEEE transactions on bio-medical engineering Jan 20, 2021
Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trai...
Algorithms Muscle, Skeletal Signal Processing, Computer-Assisted Deep Learning Action Potentials Electromyography Signal-To-Noise Ratio
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Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1.

NeuroImage Jan 15, 2021
Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simp...
Interneurons Pyramidal Cells Gamma Rhythm Databases, Factual Animals Neural Networks, Computer Action Potentials Haplorhini
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Statistical field theory of the transmission of nerve impulses.

Theoretical biology & medical modelling Jan 6, 2021
BACKGROUND: Stochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane conductance through statistical mechanics of disordered and complex systems.
Quantum Theory Computing Methodologies Artificial Intelligence Models, Neurological Action Potentials
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