AIMC Topic: Action Potentials

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Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.

eLife Mar 18, 2021
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete seq...
Neural Networks, Computer Learning Neurons Biophysical Phenomena Spatio-Temporal Analysis Models, Neurological Action Potentials
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Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement.

Circulation. Arrhythmia and electrophysiology Mar 9, 2021
Cardiac Pacing, Artificial Atrioventricular Block Heart Rate Action Potentials Aortic Valve Transcatheter Aortic Valve Replacement Risk Assessment Humans Machine Learning Predictive Value of Tests Pacemaker, Artificial Retrospective Studies Treatment Outcome Decision Support Techniques Risk Factors Prospective Studies
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Signal-to-signal neural networks for improved spike estimation from calcium imaging data.

PLoS computational biology Mar 1, 2021
Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result i...
Fluorescence Models, Neurological Action Potentials Calcium Neural Networks, Computer Algorithms
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Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons.

Neural plasticity Feb 23, 2021
Since the high dimension and complexity of the large-scale spiking neural network, it is difficult to research the network dynamics. In recent decades, the mean-field approximation has been a useful method to reduce the dimension of the network. In t...
Action Potentials Nerve Net Neural Networks, Computer Neural Inhibition Computer Simulation Neurons Models, Neurological Synapses
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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...
Feedback Models, Neurological Action Potentials Humans Dendrites Neural Networks, Computer Reaction Time Axons Synapses
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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].
Catheter Ablation Humans Retrospective Studies Middle Aged Recurrence Machine Learning Risk Factors Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Risk Assessment Treatment Outcome Atrial Function, Left Male Heart Atria Atrial Fibrillation Female Action Potentials Heart Rate Fractals Predictive Value of Tests Pulmonary Veins
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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...
Purkinje Cells Excitatory Postsynaptic Potentials Cerebellar Cortex Neurons Cerebellum Neuronal Plasticity Humans Synapses Models, Neurological Neural Networks, Computer Computer Simulation Action Potentials Synaptic Transmission Animals Interneurons Signal Transduction Neural Pathways Normal Distribution
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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...
Neurons Models, Neurological Action Potentials Least-Squares Analysis Humans Machine Learning Spatio-Temporal Analysis
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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 Algorithms Animals Neurons Electrodes, Implanted Deep Learning Action Potentials Signal Processing, Computer-Assisted
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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...
Cortical Synchronization Humans Neural Networks, Computer Action Potentials Models, Neurological Neurons
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