AIMC Topic: Action Potentials

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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Circulation research Nov 10, 2020
RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.
Prognosis Female Risk Factors Neural Networks, Computer Phenotype Prospective Studies Middle Aged Risk Assessment Diagnosis, Computer-Assisted Time Factors Aged Aged, 80 and over Male Humans Cardiomyopathies Ventricular Fibrillation Death, Sudden, Cardiac Electrophysiologic Techniques, Cardiac Signal Processing, Computer-Assisted Myocardial Infarction Tachycardia, Ventricular Support Vector Machine Action Potentials Predictive Value of Tests
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Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems.

Advanced materials (Deerfield Beach, Fla.) Nov 9, 2020
The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can ...
Neurons Synapses Brain Animals Neural Networks, Computer Humans Action Potentials Artificial Intelligence
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Evolution-Communication Spiking Neural P Systems.

International journal of neural systems Nov 7, 2020
Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distributio...
Neural Networks, Computer Models, Neurological Action Potentials Neurons
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Network structure of cascading neural systems predicts stimulus propagation and recovery.

Journal of neural engineering Nov 4, 2020
OBJECTIVE: Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between ne...
Models, Neurological Action Potentials Neurons Nerve Net Neural Networks, Computer
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Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology Oct 28, 2020
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...
Purkinje Cells Humans Cerebellar Nuclei Machine Learning Motor Activity Learning Action Potentials Conditioning, Classical Animals Cerebellum Eye Movements
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Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.

Neural plasticity Oct 27, 2020
Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification. Grayscale ...
Synapses Action Potentials Humans Supervised Machine Learning Pattern Recognition, Automated Neural Networks, Computer Neurons Models, Neurological
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Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks.

Neural computation Oct 20, 2020
Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework, tend to assu...
Nonlinear Dynamics Dendrites Neural Networks, Computer Models, Neurological Action Potentials Humans
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Recurrent amplification of grid-cell activity.

Hippocampus Oct 6, 2020
High-level cognitive abilities such as navigation and spatial memory are thought to rely on the activity of grid cells in the medial entorhinal cortex (MEC), which encode the animal's position in space with periodic triangular patterns. Yet the neura...
Humans Space Perception Models, Neurological Entorhinal Cortex Neural Networks, Computer Action Potentials Grid Cells Animals
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The Clinical Application of the Deep Learning Technique for Predicting Trigger Origins in Patients With Paroxysmal Atrial Fibrillation With Catheter Ablation.

Circulation. Arrhythmia and electrophysiology Oct 6, 2020
BACKGROUND: Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation ablation. Elimination of NPV triggers can reduce the recurrence of postablation atrial fibrillation. Deep learning was app...
Heart Rate Catheter Ablation Recurrence Pulmonary Veins Computed Tomography Angiography Phlebography Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Atrial Fibrillation Time Factors Treatment Outcome Action Potentials Humans Risk Factors Female Retrospective Studies Aged Risk Assessment Male Reproducibility of Results Adult Deep Learning Middle Aged
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Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks.

PLoS computational biology Sep 30, 2020
A basic-yet nontrivial-function which neocortical circuitry must satisfy is the ability to maintain stable spiking activity over time. Stable neocortical activity is asynchronous, critical, and low rate, and these features of spiking dynamics contrib...
Neocortex Neural Networks, Computer Action Potentials Animals Neurons Algorithms Models, Neurological Markov Chains Computer Simulation
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