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Related Concept Videos

Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

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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.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
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Graded Potential01:19

Graded Potential

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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.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
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Action Potential01:31

Action Potential

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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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Propagation of Action Potentials01:23

Propagation of Action Potentials

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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.
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...
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Integration of Synaptic Events01:28

Integration of Synaptic Events

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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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Long-term Potentiation01:25

Long-term Potentiation

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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.
Hebbian LTP
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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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Developments and further applications of ephemeral data derived potentials.

Pascal T Salzbrenner1, Se Hun Joo1, Lewis J Conway1,2

  • 1Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, United Kingdom.

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Ephemeral data-derived potentials (EDDPs) accelerate materials structure prediction using lightweight neural networks and cost-efficient training. Recent software updates enable diverse applications, including phonon and molecular dynamics simulations, enhancing materials discovery.

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Area of Science:

  • Computational Materials Science
  • Machine Learning
  • Atomistic Simulations

Background:

  • Machine-learned interatomic potentials are crucial for computational materials science.
  • Ephemeral data-derived potentials (EDDPs) offer a simple, cost-efficient approach for accelerating atomistic structure prediction.
  • EDDPs utilize small unit cell training data and lightweight neural networks for smooth, transferable interactions.

Purpose of the Study:

  • To present diverse applications of EDDPs enabled by open-source software developments.
  • To introduce new features for enhanced predictive capabilities and confidence estimation.
  • To demonstrate the versatility of EDDPs across various material systems and conditions.

Main Methods:

  • Development and application of open-source EDDP software.
  • Integration of EDDPs with phonon and molecular dynamics codes.
  • Implementation of ensemble deviation for uncertainty quantification.
  • Training EDDPs on diverse material systems (e.g., C, Pb, ScH2, ZnCN2) across wide pressure and stoichiometry ranges.

Main Results:

  • EDDPs successfully evaluated phonons, phase diagrams, superionicity, and thermal expansion in case studies.
  • New software features facilitate broader application and reliable prediction.
  • Demonstrated robustness and transferability of EDDPs for complex materials.
  • Confidence estimation using ensemble deviation aids in result interpretation.

Conclusions:

  • Recent advancements in EDDP software significantly expand its applicability in computational materials science.
  • EDDPs provide a powerful and efficient tool for accelerated structure prediction and materials property evaluation.
  • The developed methods support reliable exploration of material behavior under various conditions, complementing existing structure prediction successes.