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Related Concept Videos

Action Potentials01:41

Action Potentials

Overview
Action Potential01:14

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Action Potential01:14

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Cardiac Action Potential01:30

Cardiac Action Potential

Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials

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Updated: Jun 13, 2026

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A pilot study of ion current estimation by ANN from action potential waveforms.

Sevgi Şengül Ayan1, Selim Süleymanoğlu2, Hasan Özdoğan3

  • 1Department of Engineering, Industrial Engineering, Antalya Bilim University, Döşemealtı, Antalya, Turkey. sevgi.sengul@antalya.edu.tr.

Journal of Biological Physics
|November 13, 2022
PubMed
Summary

Artificial neural networks (ANNs) predict cardiac action potential ionic currents from waveforms. This method offers a faster, feasible alternative to traditional experiments for understanding ion channel dynamics.

Keywords:
Artificial neural networksBayesian regularizationCardiac action potentialCurrent–time dynamicsNumerical modeling

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Area of Science:

  • Computational biology
  • Biophysics
  • Artificial intelligence in medicine

Background:

  • Conventional experimental methods for capturing ion channel dynamics are often infeasible or time-consuming.
  • Understanding ionic current-time dynamics is crucial for studying cardiac action potentials (APs).

Purpose of the Study:

  • To predict ionic current-time dynamics during cardiac APs using artificial neural networks (ANNs).
  • To develop a computationally efficient method for analyzing ion channel behavior.

Main Methods:

  • Electrophysiological simulations using a single-cell model to identify ionic currents.
  • Training an artificial neural network (ANN) to predict ionic currents from AP waveforms.
  • Utilizing Bayesian regularization (BR) for model validation and refinement.

Main Results:

  • ANN accurately predicted ionic current-time dynamics solely from AP waveforms.
  • High convergence between simulated and predicted currents demonstrated the method's efficacy.
  • Bayesian approach validation scores (R values) and error analysis confirmed model reliability.

Conclusions:

  • The developed ANN methodology provides a feasible and efficient approach to predict ionic current behavior.
  • This method can be applied to any electrical excitable cell, advancing the study of electrophysiology.
  • The integration of ANNs and Bayesian solvers offers a powerful tool for biophysical modeling.