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Action Potential01:31

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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
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Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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Related Experiment Video

Updated: Jul 17, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Neural potentials of proteins extrapolate beyond training data.

Geemi P Wellawatte1, Glen M Hocky2, Andrew D White3

  • 1Department of Chemistry, University of Rochester, Rochester, New York 14627, USA.

The Journal of Chemical Physics
|August 29, 2023
PubMed
Summary
This summary is machine-generated.

Neural network (NN) coarse-grained (CG) force fields can extrapolate to new protein regions even with limited training data. This demonstrates their potential for more efficient molecular simulations.

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Area of Science:

  • Computational chemistry
  • Molecular dynamics
  • Machine learning in biophysics

Background:

  • Coarse-grained (CG) molecular mechanics force fields are essential for simulating large biomolecules.
  • Traditional CG force fields often struggle with generalizing to unseen conformational states.
  • Neural networks (NNs) offer a promising avenue for developing more robust and adaptable CG force fields.

Purpose of the Study:

  • To compare the performance of NN-based CG force fields against traditional ones.
  • To investigate the extrapolation capabilities of NN CG force fields trained on limited data.
  • To assess the relationship between force matching error and free energy surface reconstruction accuracy.

Main Methods:

  • Trained 88 NN CG force fields using diverse combinations of clustered free energy surfaces from four protein trajectories.
  • Employed atomistic simulations to generate reference free energy surfaces.
  • Utilized total variation similarity, a statistical measure, to quantify agreement between reference and NN CG free energy surfaces.

Main Results:

  • NN CG force fields demonstrated the ability to extrapolate and sample from unvisited regions of the free energy surface.
  • Training with limited data did not impede the extrapolation capabilities of NN CG force fields.
  • Force matching error showed only a weak correlation with the accuracy of the reconstructed free energy surface.

Conclusions:

  • NN CG force fields can generalize to unseen protein conformational regions when trained on limited data.
  • The findings support the hypothesis that NN CG force fields possess significant extrapolation power.
  • Force matching error is not a reliable predictor of a CG force field's ability to accurately represent the free energy landscape.