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Related Concept Videos

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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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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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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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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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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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Related Experiment Video

Updated: Oct 2, 2025

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents
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A Simplified Plasticity Model Based on Synaptic Tagging and Capture Theory: Simplified STC.

Yiwen Ding1,2, Ye Wang1,2, Lihong Cao1,2,3

  • 1State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.

Frontiers in Computational Neuroscience
|February 28, 2022
PubMed
Summary

A new simplified model of synaptic plasticity explains how memories form and stabilize. This model, based on synaptic tagging and capture, accurately simulates long-term potentiation and depression, offering an efficient learning rule for brain-like computation.

Keywords:
calcium concentrationlearning and memoryplasticity-related product (PRP)synaptic plasticitysynaptic tagging and capture

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Related Experiment Videos

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Molecular Biology

Background:

  • Memory formation relies on synaptic changes, transitioning from rapid early phases to stable late phases.
  • Synaptic tagging and capture (STC) theory explains this transition via calcium, synaptic tagging, plasticity-related product (PRP) synthesis, and capture.
  • Existing computational models lack simultaneous simplicity and biological interpretability.

Purpose of the Study:

  • To propose a simplified STC (SM-STC) model that balances biological interpretability and computational efficiency.
  • To provide a novel computational framework for understanding memory consolidation mechanisms.

Main Methods:

  • Calculated calcium ion concentration in neuronal compartments and synapses.
  • Updated synaptic tag status and PRP levels.
  • Modeled the interaction between tagged synapses and PRPs to determine synaptic plasticity (potentiation or depression).
  • Simulated the Schaffer collaterals pathway in a hippocampal CA1 neuron.

Main Results:

  • The SM-STC model successfully reproduced key experimental findings.
  • Demonstrated long-term potentiation induced by high-frequency stimulation.
  • Showcased long-term depression induced by low-frequency stimulation.
  • Replicated cross-capture phenomena with delayed stimuli.

Conclusions:

  • The SM-STC model offers an effective learning rule for brain-like computation.
  • Ensures biological plausibility while maintaining computational efficiency.
  • Provides a valuable tool for studying memory formation and synaptic plasticity.